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    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Variogram Calculation and Modeling, Spatial Estimation with Kriging Activity\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### The Interactive Workflow\n",
    "\n",
    "Here's an interactive workflow for calculating directional experimental variograms in 2D, modeling the variograms and applying the variogram models to calculate spatial estimation maps with kriging. \n",
    "\n",
    "* Wherever you see -999 values you must update with your own choices.\n",
    "* When you make changes you must rerun the subsuquent code update the results.\n",
    "* When you save your workflow and reload the interactive GUIs are reset so record you parameters before exiting, e.g., screen captures.\n",
    "\n",
    "To complete this workflow you will:\n",
    "\n",
    "1. **Load Spatial Data**, select from available datasets on Dr. Pyrcz's GitHub GeoDataSets repository or load your own dataset. Make sure that the following variables are consistent with the dataset.\n",
    "\n",
    "    - feature and feature_units - the name of the feature in the dataframe and the units of the feature\n",
    "    - vmin and vmax - the minimum and maximum values of the feature\n",
    "    - xmin, xmax, ymin and ymax - the area of interest for plotting and building a kriged map\n",
    "\n",
    "2. **Inspect the Posted Data**, visualize the data location map over the area of interest\n",
    "\n",
    "    - make sure the area of interest covers the data and does not extend too far from data (i.e. extreme spatial extrapolation)\n",
    "    - visually check for obvious sample bias with clustered samples over highs or lows\n",
    "    - visually check for minimum lag spacing and possible major direction of continuity\n",
    "\n",
    "3. **Declustering**,  apply cell beased declustering to the dataset.\n",
    "\n",
    "    - calculate the declustered mean to apply as the stationary mean for simple kriging. Recall away from data the mean gets a weight of $1-\\sum_{\\alpha = 1}^{n} \\lambda_{\\alpha}$\n",
    "    - calculate the declustered variance as the variogram sill and the maximum kriging estimation variance outside the range of all of the data (note ordinary kriging estimation variance can exceed the sill)\n",
    "    - select reasonable parameters for declustering:\n",
    "\n",
    "```python\n",
    "find_minimizing_cell_size = -999\n",
    "number_offsets = -999\n",
    "number_cell_sizes = -999\n",
    "min_cell_size = -999\n",
    "max_cell_size = -999\n",
    "```\n",
    "\n",
    "4. **Calculate Directional Experimental Variograms**, set the directional variogram calculation search template parameters to find the major and minor directions of continuity and produce the more interpretable experimental variograms possible\n",
    "\n",
    "    - use the interactive GUI to calculate the directional variograms\n",
    "\n",
    "5. **Model the Directional Variograms**, use up to 2 nest variogram structures plus nugget if present to model the directional variogram \n",
    "\n",
    "    - rerun the code to update with a new experimental variogram and declustered variance / sill\n",
    "    - use the interactive GUI to fit the directional variogram model\n",
    "\n",
    "6. **Spatial Predictions with Kriging**, use the declustering results and variogram model with the data to calculate spatial estimation maps with kriging\n",
    "\n",
    "    - rerun the code to update with a new variogram model and declustered stationary mean and variance / sill\n",
    "    - set the kriging parameters, note vdmean is the declustered mean, you can reduce ndmax to speed up the run but may add limited search artifacts\n",
    "\n",
    "```python\n",
    "skmean = vdmean                            # simple kriging mean (used if simple kriging is selected below)\n",
    "ktype = 0                                  # kriging type, 0 - simple, 1 - ordinary\n",
    "radius = -999                              # search radius for neighbouring data\n",
    "ndmin = -999; ndmax = -999                 # minimum and maximum data for an estimate\n",
    "tmin =  -1.0e21; tmax = 1.0e21             # data trimming limits, set very small and large to not trim the data\n",
    "```\n",
    "7. **Spatial Prediction with Kriging - One Location with Uncertainty Model**, use the previous kriging estimate and kriging estimation variance maps to calculate the uncertainty model at a single location\n",
    "\n",
    "    - select the location with this code:\n",
    "```python\n",
    "x = 500                                     # location to estimate\n",
    "y = 500\n",
    "```\n",
    "\n",
    "#### Additional Resources\n",
    "\n",
    "I have recorded lectures, code walkthroughs and comprehensive workflows for declustering:\n",
    "\n",
    "* [spatial bias](https://www.youtube.com/watch?v=w0HgVibxpMQ&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=25)\n",
    "\n",
    "* [declustering lecture](https://www.youtube.com/watch?v=rN0RKcTIVcI&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=26)\n",
    "\n",
    "* [declustering in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_declustering.ipynb)\n",
    "\n",
    "and variogram calculation and modeling:\n",
    "\n",
    "* [variogram introduction lecture](https://youtu.be/jVRLGOsnYuw)\n",
    "\n",
    "* [variogram calculation lecture](https://youtu.be/mzPLicovE7Q)\n",
    "\n",
    "* [variogram calculation search parameters](https://youtu.be/NE4xfhIHAm4)\n",
    "\n",
    "* [variogram calculation in Python walkthough](https://www.youtube.com/watch?v=FugSEcCi2gI&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=36)\n",
    "\n",
    "* [directional variogram calculation in Python walkthrough](https://www.youtube.com/watch?v=bryRCrtf3hk&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=37)\n",
    "\n",
    "* [variogram interpretation lecture](https://www.youtube.com/watch?v=Li-Xzlu7hvs&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=38)\n",
    "\n",
    "* [variogram modeling lecture](https://www.youtube.com/watch?v=-Bi63Y3u6TU&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=39)\n",
    "\n",
    "* [variogram modeling in Python walkthrough](https://www.youtube.com/watch?v=bRj3HnEa1Z4&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=40)\n",
    "\n",
    "* [experimental variogram calculation in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_variogram_calculation.ipynb)\n",
    "\n",
    "* [determination of major and minor spatial continuity directions in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_spatial_continuity_directions.ipynb)\n",
    "\n",
    "and kriging for spatial estimation:\n",
    "\n",
    "* [kriging lecture](https://www.youtube.com/watch?v=CVkmuwF8cJ8&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=42)\n",
    "\n",
    "* [simple kriging in Python walkthrough](https://www.youtube.com/watch?v=adkZAFKLY3s&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=44)\n",
    "\n",
    "* [kriging Interactive Demonstration in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/Interactive_Simple_Kriging.ipynb)\n",
    "\n",
    "* [Complete by-Facies Kriging Workflow for Spatial Estimation in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_kriging.ipynb)\n",
    "\n",
    "Here's some more basic details on each topic for convenience. \n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Variogram Calculation Parameters\n",
    "\n",
    "The variogram calculation parameters include:\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth (isotropic variograms are calculated with an azimuth tolerance of to 90.0)\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance, usually set to the minimum data spacing\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance, commonly set to 50% of unit lag distanceonal smoothing\n",
    "\n",
    "* **number of lags** - set based on the spatial extent of the dataset, we can typically calculate reliable variograms up to 1/2 the extent of the dataset\n",
    "\n",
    "* **bandwidth** is the maximum offset allowable from the lag vector \n",
    "\n",
    "\n",
    "#### Variogram Modeling\n",
    "\n",
    "Spatial continuity can be modeled with nested, positive definate variogram structures:\n",
    "\n",
    "\\begin{equation}\n",
    "\\Gamma_x(\\bf{h}) = \\sum_{i=1}^{nst} \\gamma_i(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "where $\\Gamma_x(\\bf{h})$ is the nested variogram model resulting from the summation of $nst$ nested variograms  $\\gamma_i(\\bf{h})$.\n",
    "\n",
    "The types of structure commonly applied include:\n",
    "\n",
    "* spherical\n",
    "\n",
    "* exponential\n",
    "\n",
    "* gaussian\n",
    "\n",
    "* nugget\n",
    "\n",
    "Other less common models include:\n",
    "\n",
    "* hole effect\n",
    "\n",
    "* dampenned hole effect\n",
    "\n",
    "* power law\n",
    "\n",
    "these will not be covered here.\n",
    "\n",
    "Each one of these variogram structures, $\\gamma_i(\\bf{h})$, is based on a geometric anisotropy model parameterized by the orientation and range in the major and minor directions.  In 2D this is simply an azimuth and ranges, $azi$, $a_{maj}$ and $a_{min}$. Note, the range in the minor direction (orthogonal to the major direction).\n",
    "\n",
    "The geometric anisotropy model assumes that the range in all off-diagonal directions is based on an ellipse with the major and minor axes alligned with and set to the major and minor for the variogram.\n",
    "\n",
    "\\begin{equation}\n",
    "\\bf{h}_i = \\sqrt{\\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2 + \\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2}  \n",
    "\\end{equation}\n",
    "\n",
    "Therefore, if we know the major direction, range in major and minor directions, we may completely describe each nested componnent of the complete spatial continuity of the variable of interest, $i = 1,\\dots,nst$.\n",
    "\n",
    "Some comments on modeling nested variograms:\n",
    "\n",
    "* we can capture nugget, short and long range continuity structures\n",
    "\n",
    "* we rely on the geometric anisotropy model, so all structures must inform the same level of contribution (porportion of the sill) in all directions.\n",
    "\n",
    "* the geometric anisotropy model is based on azimuth of the major direction of continuity, range in the major direction and range in the minor direction (orthogonal to the major direction).  The range is interpolated between the major and minor azimuths with a ellipse model\n",
    "\n",
    "* we can vary the type of variogram, direction or azimuth of the major direction, and major and minor ranges by structure\n",
    "\n",
    "#### Spatial Estimation\n",
    "\n",
    "Consider the case of making an estimate at some unsampled location, $𝑧(\\bf{u}_0)$, where $z$ is the property of interest (e.g. porosity etc.) and $𝐮_0$ is a location vector describing the unsampled location.\n",
    "\n",
    "How would you do this given data, $𝑧(\\bf{𝐮}_1)$, $𝑧(\\bf{𝐮}_2)$, and $𝑧(\\bf{𝐮}_3)$?\n",
    "\n",
    "It would be natural to use a set of linear weights to formulate the estimator given the available data.\n",
    "\n",
    "\\begin{equation}\n",
    "z^{*}(\\bf{u}) = \\sum^{n}_{\\alpha = 1} \\lambda_{\\alpha} z(\\bf{u}_{\\alpha})\n",
    "\\end{equation}\n",
    "\n",
    "We could add an unbiasedness constraint to impose the sum of the weights equal to one.  What we will do is assign the remainder of the weight (one minus the sum of weights) to the global average; therefore, if we have no informative data we will estimate with the global average of the property of interest.\n",
    "\n",
    "\\begin{equation}\n",
    "z^{*}(\\bf{u}) = \\sum^{n}_{\\alpha = 1} \\lambda_{\\alpha} z(\\bf{u}_{\\alpha}) + \\left(1-\\sum^{n}_{\\alpha = 1} \\lambda_{\\alpha} \\right) \\overline{z}\n",
    "\\end{equation}\n",
    "\n",
    "We will make a stationarity assumption, so let's assume that we are working with residuals, $y$. \n",
    "\n",
    "\\begin{equation}\n",
    "y^{*}(\\bf{u}) = z^{*}(\\bf{u}) - \\overline{z}(\\bf{u})\n",
    "\\end{equation}\n",
    "\n",
    "If we substitute this form into our estimator the estimator simplifies, since the mean of the residual is zero.\n",
    "\n",
    "\\begin{equation}\n",
    "y^{*}(\\bf{u}) = \\sum^{n}_{\\alpha = 1} \\lambda_{\\alpha} y(\\bf{u}_{\\alpha})\n",
    "\\end{equation}\n",
    "\n",
    "while satisfying the unbaisedness constraint.  \n",
    "\n",
    "#### Kriging\n",
    "\n",
    "Now the next question is what weights should we use?  \n",
    "\n",
    "We could use equal weighting, $\\lambda = \\frac{1}{n}$, and the estimator would be the average of the local data applied for the spatial estimate. This would not be very informative.\n",
    "\n",
    "We could assign weights considering the spatial context of the data and the estimate:\n",
    "\n",
    "* **spatial continuity** as quantified by the variogram (and covariance function)\n",
    "* **redundancy** the degree of spatial continuity between all of the available data with themselves \n",
    "* **closeness** the degree of spatial continuity between the avaiable data and the estimation location\n",
    "\n",
    "The kriging approach accomplishes this, calculating the best linear unbiased weights for the local data to estimate at the unknown location.  The derivation of the kriging system and the resulting linear set of equations is available in the lecture notes.  Furthermore kriging provides a measure of the accuracy of the estimate!  This is the kriging estimation variance (sometimes just called the kriging variance).\n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^{2}_{E}(\\bf{u}) = C(0) - \\sum^{n}_{\\alpha = 1} \\lambda_{\\alpha} C(\\bf{u}_0 - \\bf{u}_{\\alpha})\n",
    "\\end{equation}\n",
    "\n",
    "What is 'best' about this estimate? Kriging estimates are best in that they minimize the above estimation variance. \n",
    "\n",
    "#### Properties of Kriging\n",
    "\n",
    "Here are some important properties of kriging:\n",
    "\n",
    "* **Exact interpolator** - kriging estimates with the data values at the data locations\n",
    "* **Kriging variance** can be calculated before getting the sample information, as the kriging estimation variance is not dependent on the values of the data nor the kriging estimate, i.e. the kriging estimator is homoscedastic. \n",
    "* **Spatial context** - kriging takes into account, furthermore to the statements on spatial continuity, closeness and redundancy we can state that kriging accounts for the configuration of the data and structural continuity of the variable being estimated.\n",
    "* **Scale** - kriging may be generalized to account for the support volume of the data and estimate. We will cover this later.\n",
    "* **Multivariate** - kriging may be generalized to account for multiple secondary data in the spatial estimate with the cokriging system. We will cover this later.\n",
    "* **Smoothing effect** of kriging can be forecast. We will use this to build stochastic simulations later.\n",
    "\n",
    "In this workflow we will explore methods to:\n",
    "\n",
    "1. decluster the data to calculate a representative mean\n",
    "2. interactively detect directionality from a spatial dataset\n",
    "3. interactively calculate the directional variograms in the major and minor directions \n",
    "4. interactively build a consistent 2D model fit to the major and minor directions\n",
    "5. apply the variogram model for building an spatial estimated map with simple kriging with the representative mean\n",
    "\n",
    "Note, since we are using the variogram for estimation, we will not Gaussian transform the feature first.\n",
    "\n",
    "* the sill of the variogram will be equal to the variance of the data and not necesssarily 1.0 as with the standard normal distribution.\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "from matplotlib.pyplot import cm                        # color maps\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox\n",
    "from scipy import stats                                 # summary statistics\n",
    "from statsmodels.stats.weightstats import DescrStatsW   # any weighted statistics\n",
    "from scipy.stats import norm                            # Gaussian distribution\n",
    "import math                                             # square root \n",
    "\n",
    "import warnings                                         # remove warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"c:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.131888</td>\n",
       "      <td>1.996868</td>\n",
       "      <td>5590.417154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.767608</td>\n",
       "      <td>10.711789</td>\n",
       "      <td>3470.845666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.591186</td>\n",
       "      <td>17.818143</td>\n",
       "      <td>3586.988513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.616662</td>\n",
       "      <td>217.109365</td>\n",
       "      <td>3732.114787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.608824</td>\n",
       "      <td>16.717367</td>\n",
       "      <td>2534.551236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X      Y  Facies   Porosity        Perm           AI\n",
       "0  100.0  900.0     0.0  10.131888    1.996868  5590.417154\n",
       "1  100.0  800.0     1.0  14.767608   10.711789  3470.845666\n",
       "2  100.0  700.0     1.0  14.591186   17.818143  3586.988513\n",
       "3  100.0  600.0     1.0  18.616662  217.109365  3732.114787\n",
       "4  100.0  500.0     1.0  14.608824   16.717367  2534.551236"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = 1\n",
    "\n",
    "if data == 0:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v6_sand_only.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df = df.iloc[:,1:]                                      # remove first column\n",
    "elif data == 1:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_MV_biased.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df['Porosity'] = df['Porosity']*100.0\n",
    "    df = df.iloc[:,1:]                                      # remove first column\n",
    "elif data == 2:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v3.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df = df.iloc[:,1:] \n",
    "elif data == 3:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/12_sample_data.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s) \n",
    "    df['Porosity'] = df['Porosity']*100.0\n",
    "    df = df.iloc[:,1:] \n",
    "elif data == 4:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v5_sand_only.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s) \n",
    "    df = df.iloc[:,1:] \n",
    "else:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v1.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df = df.iloc[:,1:] \n",
    "    \n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features:\n",
    "\n",
    "* **X** - x coordinate in meters\n",
    "* **Y** - y coordinate in meters\n",
    "* **Porosity** - rock porosity averaged over a specific rock unit from a vertical well\n",
    "* **Perm** - rock permeability averaged (scaled up) over a specific rock unit from a vertical well \n",
    "* **AI** - acoustic impedance from a seismic cube assigned at a specific rock unit and at the location of a vertical well \n",
    "* **facies** - facies, 0 - shale, 1 - sandstone\n",
    "\n",
    "Concerning facies:\n",
    "\n",
    "We will work with all facies pooled together. I wanted to simplify this workflow and focus more on spatial continuity direction detection. Finally, by not using facies we do have more samples to support our statistical inference. Most often facies are essential in the subsurface model. Don't worry we will check if this is reasonable in a bit.   \n",
    "\n",
    "You are welcome to repeat this workflow on a by-facies basis.  The following code could be used to build DataFrames ('df_sand' and 'df_shale') for each facies.\n",
    "\n",
    "```p\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index()  # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "```\n",
    "\n",
    "Let's look at summary statistics for all facies combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>368.0</td>\n",
       "      <td>499.565217</td>\n",
       "      <td>289.770794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>762.500000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>368.0</td>\n",
       "      <td>520.644022</td>\n",
       "      <td>277.412187</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>769.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>368.0</td>\n",
       "      <td>0.597826</td>\n",
       "      <td>0.491004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>368.0</td>\n",
       "      <td>12.702582</td>\n",
       "      <td>3.064227</td>\n",
       "      <td>4.112152</td>\n",
       "      <td>10.341201</td>\n",
       "      <td>12.584177</td>\n",
       "      <td>14.862292</td>\n",
       "      <td>21.025763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>368.0</td>\n",
       "      <td>85.617362</td>\n",
       "      <td>228.362654</td>\n",
       "      <td>0.094627</td>\n",
       "      <td>2.297348</td>\n",
       "      <td>10.377292</td>\n",
       "      <td>50.581288</td>\n",
       "      <td>1991.097723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>368.0</td>\n",
       "      <td>4791.736646</td>\n",
       "      <td>974.560569</td>\n",
       "      <td>1981.177309</td>\n",
       "      <td>4110.728374</td>\n",
       "      <td>4713.325533</td>\n",
       "      <td>5464.043562</td>\n",
       "      <td>7561.250336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean         std          min          25%  \\\n",
       "X         368.0   499.565217  289.770794     0.000000   240.000000   \n",
       "Y         368.0   520.644022  277.412187     9.000000   269.000000   \n",
       "Facies    368.0     0.597826    0.491004     0.000000     0.000000   \n",
       "Porosity  368.0    12.702582    3.064227     4.112152    10.341201   \n",
       "Perm      368.0    85.617362  228.362654     0.094627     2.297348   \n",
       "AI        368.0  4791.736646  974.560569  1981.177309  4110.728374   \n",
       "\n",
       "                  50%          75%          max  \n",
       "X          500.000000   762.500000   990.000000  \n",
       "Y          539.000000   769.000000   999.000000  \n",
       "Facies       1.000000     1.000000     1.000000  \n",
       "Porosity    12.584177    14.862292    21.025763  \n",
       "Perm        10.377292    50.581288  1991.097723  \n",
       "AI        4713.325533  5464.043562  7561.250336  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                               # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the Model Parameters\n",
    "\n",
    "See the the following model parameters:\n",
    "\n",
    "* **xmin**, **xmax**, **ymin** and **ymax** - extents of the dataset for plotting\n",
    "* **feature** and **feature_units** - feature of interest and associated units\n",
    "* **vmin** and **vmax** - minimum and maximum of the feature of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0; xmax = 1000.0                                # spatial extents in x and y\n",
    "ymin = 0.0; ymax = 1000.0\n",
    "feature = 'Porosity'; feature_units = '%'         # name and units of the feature of interest\n",
    "vmin = 0.0; vmax = 22.0                                  # min and max of the feature of interest\n",
    "cmap = plt.cm.inferno                                    # set the color map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspection of Posted Data\n",
    "\n",
    "Data visualization is very useful to detect patterns. Our brains are very good at pattern detection. I promote quantitative methods and recognize issues with cognitive bias, but it is important to recognize the value is expert intepretation based on data visualization.\n",
    "\n",
    "* Look for clustering of samples over regions of high or low values.\n",
    "\n",
    "* This data visualization will also be important to assist with parameter selection for the variogram calculation search template.\n",
    "\n",
    "Let's plot the location maps of the original feature and the normal score transforms of the feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(111)                                        # location map of normal score transform of porosity\n",
    "GSLIB.locmap_st(df,'X','Y',feature,0,1000,0,1000,vmin,vmax,feature,'X (m)','Y (m)',feature,cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.1, wspace=0.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look carefully, and you'll notice the the spatial samples are more dense in the high porosity regions and lower in the low porosity regions.  There is preferential sampling.  We cannot use the naive statistics to represent this region.  We have to correct for the clustering of the samples in the high porosity regions. \n",
    "\n",
    "Let's try cell declustering. We can interpret that we will want to minimize the declustering mean and that get a sense of a range of possible optimal cell sizes based on 'an ocular' estimate of the largest average spacing in the sparsely sampled regions.   \n",
    "\n",
    "Let's check out the declus program reimplimented from GSLIB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.declus(df, xcol, ycol, vcol, iminmax, noff, ncell, cmin, cmax)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.declus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now populate the parameters. The parameters are:\n",
    "\n",
    "* **df** - DataFrame with the spatial dataset\n",
    "* **xcol** - column with the x coordinate\n",
    "* **ycol** - column with the y coordinate\n",
    "* **vcol** - column with the feature value\n",
    "* **iminmax** - if 1 use the cell size that minimizes the declustered mean, if 0 the cell size that maximizes the declustered mean\n",
    "* **noff** - number of cell mesh offsets to average the declustered weights over\n",
    "* **ncell** - number of cell sizes to consider (between the **cmin** and **cmax**)\n",
    "* **cmin** - minimum cell size\n",
    "* **cmax** - maximum cell size\n",
    "\n",
    "We will run a very wide range of cell sizes, from 10m to 2,000m ('cmin' and 'cmax') and take the cell size that minimizes the declustered mean ('iminmax' = 1 minimize, and = 0 maximize). Multiple offsets (number of these is 'noff') uses multiple grid origins and averages the results to remove sensitivity to grid position.  The ncell is the number of cell sizes.\n",
    "\n",
    "The output from this program is:\n",
    "\n",
    "* **wts** - an array with the weigths for each data (they sum to the number of data, 1 indicates nominal weight)\n",
    "* **cell_sizes** - an array with the considered cell sizes\n",
    "* **dmeans** - an array with the declustered mean for each of the **cell_sizes**\n",
    "\n",
    "The **wts** are the declustering weights for the selected (minimizing or maximizing cell size) and the **cell_sizes** and **dmeans** are plotted to build the diagnostic declustered mean vs. cell size plot (see below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 368 data with:\n",
      "   mean of      12.702581678766817 \n",
      "   min and max  4.11215184025049 and 21.02576261742328\n",
      "   standard dev 3.0600605579028985 \n",
      "\n",
      "Porosity Declustering Results:\n",
      "Stationary Mean: naive mean is 12.703, declustered mean is 12.133.\n",
      "Variance/Sill: naive variance is 9.364, declustered variance is 9.523.\n",
      "Declustering correction in mean of 4.48%.\n"
     ]
    },
    {
     "data": {
      "image/png": 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sxIcfdMXPL5HVq1eapb4nHn+e76ZtICkpDYCcHAPfz1jHAw/0MVuva3E0Gg09ejzKDzPXYzDk9oolJKTw/XebeHzQsxUSU2n9vnQJPuHBvNmkNt0DqzO4QQBPOFsw+eMPTVbHrl27iN2zhY9a1+KBOtV5tqE/79R145O3XjdZHYqiKIqiKFWdaoCWkF6vJzg4mISEhALpUkquXbtGePhttycttePHj9O0kS0uzjeG8Gm1Gnr39GTL5g0mr++/KDs7m+DgYJKSkkqcx2AwEBp6lubNqxVI79evIZs2mWd3h6CgIJ577nVeHfUrI4b/yHPPzMTBPojBLw4zS30lNWzoK9hYN+DZp2cyYviPvPHqUgYPfpMGDRpUaFx3a/PqVTxSs2CvbXNvd84fPYKpFmLbtPoPHvV3LfDAoLqzPVZpiURHV5VtKSXSaCj1S1EURVEUpThqCG4J/PX3WhYtmk3t2h6EhydQrVo9xr/3MVevXuXTz8bj4qJFGiUpKYIPP5xEjRo1TFKvTqdDry/85VifY0SnU1s+FGfV6j9YsmQedep4cvVqPIGBQYx798Nit6cRQiCNuQ8Xbm5M5OgNZn3fO3fqQqeOnUlPT8fa2hqtVlt8JjPTaDS8PHwUQ4eMIDMzE1tb2wrrkS0LrU6L3mg0ax06Cwv0hiL+vRqNFTaM+u5JUIsQKYqiKIpiRlXlW1GFOXHiBGvXLmD+gmFYW+c2XFb+sYevv/6MU6eP8vkXA/D39wTg4sVwxr33Gj8vWmGSxkPjxo2Z9FkO4ZGp+HrbA5CRoWf1+hi+nvpQmcv/Lzt48CCbNy9lwaKXsbTMbTQuWbyD72Z8w1tvvnvHvBqNhgYNW7JjxyU6d87dh1NKyeJfj/HQQ4PNGrcQAjs7O7PWURparbZSxlVSjzz+FH/MmcawRrXyG9A7rkbSvEMnkzWoHxn0JHPHjqKRlwuavDJPRydg6VUNV1dXk9RhdhLUNpCKoiiKopiTaoAW44+VSxn2ctf8xidAv/7tWPjTJLp2D8pvfALUru1Ls2be7Nu3j/vvv7/MdWs0Gj78ZDrjJ4ymWZA1ttaCfw8k8eJLY/Hx8Slz+f9lf6xcwoiRPfIbnwBPPNmBZ56agdH4DhrNnUefv/32BMaOHcn27duoWdOBgwejqd+gPQ899Ii5Q1fMoHe/fpw6cpgPdm2jsb01oVk5ZLp7M3n8+yaro0WLFrQe9BxvLfmZ1i7WxGQbuaq1Y/LcaSarQ1EURVEUpapTDdBiJCXF4+FRv0CaEAIbW0ucnQpvr+DhYX9X8w2LU79+fX5Zsp6DBw+SmZnJS6NaYW9vb7Ly/6uSkhLw8HAqkKbRaLCxsUSv12NlZXXH/I6Ojsya9TOnT58mMjKSvv0aqO1vqjCNRsN7Ez8lLCyMs2fP8pCPDw0aNDD5cOKXRoyk76AnOHbsGM7OzjRr1gyNRkNWVhbz5s9m167NSClp164zw4a+gq2trUnrLzsJRrUKrqIoiqIo5qMWISpG27ad2LjhWIG0yMh4NBo79uwOwXjTvDKj0cj2bedp1aqVSWPQ6XS0a9eOLl26qMZnCbVt25GNG44USLt6NQZLS4diG5/XCSFo1KgR3bt3V43P/wg/Pz+6d+9Ow4YNzTaX1c3NjW7dutGiRYv8nvZ3x72Bk3ME834awoJFw6nun8wbb4402QJIpiOR0lDql6IoiqIoSnFUD2gx+vUdwJhX15OZuY5OnRty9Wosv/68l//9byK7/93OuHcXM+jxthiNRpYs3kvnzr1Ntq+gUnqPDXyC0WOGkpq6kfYd6nP5chSLf9nPhPe/qujQlHvM2bNn0emSefKp3vlpffu14+SJMA4dOmTyB1ZlpvYBVRRFURTFjFQDtBhWVlbM+G4u/2zayJpV+/Dw8OabKT/i7e1N82bNOXz4MP/88xcajYZhQ9+nSZMmFR2ySSUnJ7N6xXLOHztKYMMg+g0ahIuLS0WHVSxbW1t+mPkT6zesY/XKQ3h5+TLt2/l4enoWn7mUpJTs3r2bTatXY2FlxaODBv3nPg/K3QsJCaFxE79C6UGNfQgOCa5cDVApQW2noiiKoiiKGakGaAlYWlryyMOP8sjDjxY61qJFC1q0aFEBUZlfdHQ0o599ku72Gnq5OnBhwxle/m0xUxb8TPXq1Ss6vGJZWVnRt08/+vbpVy71TfrwQyJ37qCntxfZBgPfjh5F1xdf5LmXhpRL/UrlVKtWLbZs/bVQ+onj4fR+tH8FRKQoiqIoilJx1BxQ5bbmTv+WJ92t6F27GrVcnXiwlh9Dqjnww2Q1jPVW58+f59L2bYxu1ID67m408fLkncaNWPvTTyQmJlZ0eEoFqlu3LlK68MvPW8nK0qPX57Bi+b9ERkqaN29e0eHdQoI0lP6lKIqiKIpSDNUAVW7r2L49tPErOGTV3daaHdu2cuTIkUq4gErJpaamsmXLFnbs2EFWVlaJ8ly4cIH169dz/vz5Qsf27dlDW0eHAgvb6DQamtrbcfToUVOFbRKxsbFs3LiRffv2FVhESzGfzz+bQnZWTYYPXcBLL84jJtqDb6Z8b7aFkMpCSEOpX4qiKIqiKMVRQ3CV27JzcCQpMxsXGyuklMzef5Iz1+JobW3Pz2PfYaq9Hd/On4+rq2tFh3pXNm3YwMxPP6G1gx16Kfk2I4vxk7+h5W3m4mVnZzN21KsknQnBX2vPUkMqDnX9mfzDjPwVdV1cXTmVU3jxlvicnEo1Z3bhvB/YsnI2HWvpiU3TMv0LVyZ9VzWGVFdllpaWDBv6CsOGvlLRodyZmgOqKIqiKIqZqR5Q5bYee3EIP50JxWA0sutKBPGxabxSsxaPN27My3Xr8YhOx6fjxlV0mHclPj6eHz75mImN6/NEnUCerVuL9+rW5LO33yI7O7vIPPNmzcbpQgxDAlrTs3oDhgS0xi0kgbnf/5B/Trfu3dmXkUlEamp+2rnYOCKsbGjcuLHZr6skjh8/zsG/ZzL3RWte7OzI2w/bMb5HIh+/W8kbRYqiKIqiKMp/hmqAKrf1cO/eNHzsGd44cInvDpyllZMblk6uuLq6AdDM05vQU6du23CrjLZs3kwXFwdsLCzy01xtbAiytuTgwYNF5tm05m86+9QpkNbJuw6b1/6d/7utrS1fzJ7DrJg4Jp06w6cnTrFaCqbMnZu/F2RFW79mKU+1AY3mxrDPur422GSHERERYda609PTSUtLM2sdpnS7ePV6PUlJSVV6+PmdydxtWEr7UhRFURRFKYYagqvclhCCl0aM5OkXX2L088/jb2mNh6NjwXOgSs0jNOTkoKHwvDutAIOh6KGHRoMBzS1z9YQQhRoh9erV45e1fxIREYFOpzPrli+lYTQY0FkXce1aYba/w7i4OD59913Cz51Bg8DRz4/3v/q60g75jYmJYcLY8Vw5fQmN0OBV05eJUz7H29ubKZMm88+ajVhjibTV8PaHY+nSpUtFh2xyai6noiiKoijmpBqgSrGsra3p/9xz/PPtNF68qQF6Li4Wr9p1sLa2rsDo7k7X7t0Z/f13dDcYsNRqAUjJyuZYWib/a926yDydH+zJng0H6eh7oxd0b2QwHXv1KHSuEAJfX1/zBF9G3R9+jGVT/qZ5TZm/+E1oTBYJBjezxCyl5M2hQ+hrpaFZ8yAALsYn8OZLg/n1r7+xtLQ0eZ1lYTQaGTV4BA1SfLjPpxcAYdFRjHhuGB27d+bUqqP08+iFRmhI12fw/ivj6P1Cf7p07YyFhQUnT5zCv0Z1OnTogE5XRf9rlRKq0AMlRVEURVGqnir6LUkpbw898gh7tm1j8qFDNLOxJTLHwHkNfPvt1IoO7a54enry1GtvMGHaVDo6OaCXkn9T0njzsy9u25AePmYUow8PI/TKYWpo7Qk1ppHh5cDM18eUc/Rl06pVK/a0fpIxvyyje90sYlJ1bL/swKffzjTLaqzHjx/HPS2FZv43Gu61XV1oGhvP9u3b6dmzp8nrLIujR49iGWck0PNG76yfgxdukVf449cVPBcwEI3QYDAYiLgSTs1UX5ZN/Y15U+Zhr7GnsW8T0nXpfOMylfmL5+Hu7l6BV1N6qgdUURRFURRzUg3QKmb7tm38PGs+CXHxtOvSgSGvDC+XVWg1Gg2fTpnCuXPnOHrkCAEODridOsUbg1/C3tGRQYNfpNeDD1bKbSVu1X/QIDp06cLOHTvQWVgwrEsXHG8ZWnwzW1tb5i39hUOHDnHp4kV61KpFq1atqsS13kwIwatvjefKY8+yb99eaju7MKRz5/yVfE0tJiYGL522ULqXTku0meeclkZsbCz20qZQunWOJZocgUbkzuWNioxCq9fhZeXFoYzT1NDWpJZlLeykHZ4unlxJusKnEz7li2++YMGPC9i4dj1W1lY88cJT9BvQr8p9bhRFURRFUUxJNUCrkKW/LGb19IU84NUEZ6dAjm88y5Ctz7Jo5W84ODiUSwz16tUjICCAlx4bRHsjjPMPJCU7i98mfUVocAjDRo0slzjKysPDgwEDB5b4fCEErVq1otVttmqpSmrUqEGNGjXMXk+TJk2Yn5rOACkLzKE9lJbBa7cZ7lyRGjduzDRjDO2kLNBIjNIkYufuQFp2OnaWtqSmpOKodeR02nkyc7Kp41AHS2FFUmISnp6e+Dv6s37/Xwx9Zgi213R0cm2LPkPP0s9/5uzJM4z78L0KvMriqG1YFEVRFEUxr8qxPKdSLL1ez88zf2SQfzvcbBzRarQ096pFvWxnVi7/o1xjWffXXzTIzqGrfwCWWi1uNraMaBDEuiWLq9RKp4p5eXp6cl+fvkw/cYaQhESuJiXz4+nzeDRvRYMGDSo6vEJ8fHzo0q8Hf0f8S2RqDDFp8fwTsZeGnZvx0Vcf80/CTi4nhpJmyOBU6nku6q/ibO2CURqQSMhb3EoiSUxJwngtm2YejbHUWmBnaUtHr3Zs/3MrsbGxFXuhdyLJbYCW9qUoiqIoilKMe74HNCEhgb/X/Ul8fAytWt5H27ZtS7VthtFoZMnPP7N++RKys7Jp1607Q0aOuePQzrsRHR2Ni8YWC03Bv7Ja9l4cP3CY021b891X33I1+Are1XwZ+fYYWrRogZSSI0eOsH3TNhydnejdr3eZV2c9ffgwDR2dCqRphCDAxo6rV69Sv379MpV/t3bt3MmC72aQFBtLzQb1GfHWWwQGBpZL3bGxsfww9TsO/LsHWzs7OjzQFWOOESsrKx7q/Qj+/v7lEkdFCA4OZtaUKQSfOYuzuzsvjhlNh44dATh9+jSb123AysaejiPHsHHrVnJy9PR84216PfhgmevW6/UsmvcT61asxmgw0PWRBxk68mVsbAoPob0bb497hx33t+WPxcsx5Bh4dtDL9HqgFxqNhu+XzuLnHxdxesNhiNfQv8EgLsZd4EzYGepa18fF0xmAS4kX8a3ug1dawTmgQgg8tG5cvHix0s4PFUiEVIsQKYqiKIpiPuK/up9dq1at5O32dbzu+InjfP75OAYMbI6PrwtbNp8hNcWOSV98g1ZbeO7anXw+YTwc3cGT9f2w0mnZdjmKjVm2zFv2h0lWxMzIyGBg14d5uXqX/LloAIeiLmK8vxr7N+2hh1NzfOw9iE6LZ1PCIT6Y8Rl/rvqTk5uOEmDhT6YxkwvGy3w8dSLtO7QvdSy/LlxIzNLlPBBQMz9NSsnHp44xa81qXFxcynStd2PD33+z9PMvGFyrNh62dpyLi2NRRBjTFv9q9q0+0tLSeKbPY7ST3gS5+7P23D6OhQXT3rcRDs6OHMq8yrBxr9Knfz+zxlERrl69ymtPP8PzPtWo6+pGTHoaPwVf4Kn3xnHu9Fl2L/+bNna+ZBsN7EkPY/j7b/FI794mq/+tkWPQngink3c9NELD3qgLXPOxYN6Sn80+xzIrK4tXX36Va6fCcDA4cjTiCBZYEOQTRJo2DaeaTvQd1Ie1k1fQxr1FgbzrY7Yyc8Vsk302hRCHpJQmGxfesomX/HftE6XObxPwnUnjUSqfktxbFUVRlHtTSb+X3LNDcKWUTJk8kcnfPM3Ax9pz//0NeX/CQLy89fyzaeNdlRUdHc25f7fyUuMa2FlaoNNo6BHoQz1DEtu2bjVJvDY2NvTo9zAbwo6iN+Ru+B6WEstB/TWuXr6W3/gE8LRz5SG3tnw87kNObTpGD6/O1HatSZB7Ax506cLEdz++7Z6XJdG7f392ZqZxPi53KKHeYGBl8EUadehQro1PgPnfTmNU/YZ42tkjhKC+uzuDPDz5efZss9e9ZuVq6usdaeIRQGx6MpeiwxhRvTu+RnuaetTihWqdmDVpGqmpqWaPpbwtmj2bx9y9qOfmjhACTzt7RtVrxPQvJrFr+V8MrXkfzbwCaONTixE17uOHz6eQnp5ukrqDg4OJOHaentWCsNJZYKHV0tG3PrpriRw6dMgkdRTlr7V/MeChATzatTdxcXFkW2UTnBWMf11/qjeozqWsYFJ0KWRlZfHDlFlsv7SHE6GnkFJilEaOxZ4koFlgpd0DVVEURVEUpTzcsw3QmJgYHBw1+PgUXEH2kUebs2vX5rsqKyQkhHqOloV6Xho6WnHx9Kkyx3rd6++8RYvnH2JB7B5mhW7hiFsy7371MRfOnsPR0r7AuS42jlwNDqWWVUCBdBsLGxxy7Lh48WKp43B0dGTqwgVsc7LjvRNH+Oj8aVwfeZD3Jn5S6jJLw2AwYExPx/6W/STru7lz7uRJs9d/5ugJatjmDqU8FRNKkG01NBqBhUaLXq/HQqsj0MKdw4cPmz2W8nb+xEnquxUcRmpvaUlsZBTNrD0LLDpkpbOgjqULR48eNUndFy9exF9beGh7NY0958+eK1WZ2dnZHDlyhNOnT1PUqJDFvyzmuwkzqZ/RjAB9XU5tP4f9RQ9a23bk8pGraE/Ycb9dN8LPRJH8bzZN9e3oU+dJtkXtY+6ZX1ib8A+1ejdg8ndTShVf+ZEgjaV/KYqiKIqiFOOenQNqbW1NWlpWofTk5HRsbe2LyHF7/v7+XEzVF0o/l5xN07p1Sx3jrTQaDUNHDGfoiOFkZmbyzqtv8/Vbn2ETq2F26O8E+dSmZ637EEKQlJmCrYMdmYbC15gls7G1tS1TLP7+/kydO7dMZZSVVqtFWlmRrtdja2GRn34hPp7AevXMXn+dRvU5e2A9NZw8sdJZkGLUgwS9NGCRN+w6S+aU+b2ujALr1+PChRCaeHnnp6Xr9WitrcgsYh/JdBO+DwEBAYQZUgqlhxvT6VW71l2Xt33bdj4dNxFX6YwBA3oHA9/OmUbNmrlDzI1GI/Nn/kQHt+7oNDoOhu6jtX0HdOjYF/wvjW1a4qJz42DwPmpa1MXfKpCY6Fhq163NE0GD2ZG6gbXb/jLbdjcmJQGj+RqSQoj5wKNAtJQyqIjjY4Fn8n7VAQ0Aj7zXbzedGgh8IKX8VgjRDJgFWAM5wEgp5X6zXYSiKIqiKGVyz/aAOjo64u7uz44dN3rKsrP1LJi/kz69H7ursnx8fKjWrA1LTl8lK8eAUUp2XYniuNGGbt17mDp0AKZOmoI8mUpfry70q9+Lni7tCI+J5kT0BRIzU1gXu4+xH77LOf0lsg3Z+fmuJYdj7+v4nxkG+Pyokcw6e4bEzEwAriQl8ltUOM+PGGH2uvsO7M8JEce5uGs09azJsbSrhGck4ODkhEarJSotgWirDFq0aFF8YVXMcy+/zLKocK4kJQKQmJnJ7HOnGT32bQ5nRZOSnZF/bnhKPLHWRpo0aWKSuuvWrYtj3WpsDz9LjjH339uBqEukeVjTpk2buyorLi6OT97+mJ72neng2pbOrvfTTN+Q14a9ijGvIZaamoo2R4cubwGwrOwsbLV26DQWJGYm4Grhjk6jIykrEVedGxohMBpy82qEBlvsiYuLM8m1m59EGA2lfpXAAuC2q1BJKb+WUjaTUjYDxgHbpZTxUspzN6W3BNKBlXnZvgI+zjv2Qd7viqIoiqJUUvdsDyjA++MnMv79t1m98jA+Ps4cPXqVxx57gaCgQg/mi/XBpK/5afYsxq5egTEnh6bt2vPd2+9gecvwUFPZsm4zT3j2BHLnh1YLqE721Rz+uLSVNve34X9TP6JDxw442Dsw+eOvcMOFLJmFlbcN02fPMEtMFaHPgAFYWlszc9Zs0pOT8AkI4NO5c/N7r8zJ0dGRWYsXMG3SZLYc3o9NDQ9WppyhETUwRISSYmNk6o8zS7WqcmUXGBjIpz/OZeaXXxJ+5jh2jk488967PPDQQ9SqW5dP3xmPH7ZkSwMpdlqmzPnepO/DN7O+Z9b0Gcz6cz1Go5H7u3dh5luv33Ud6/5aR6DGHxsL6/w0d1s3rOItOHnyJE2aNMHe3h6jzoDeoMdCa4G1pTVphlQssMDZ2oV4fSwuOrf8n+21Tmi0uXEYpZF0UivtqrflTUq5QwgRUMLTnwKWFJHeHbgkpbxyvVjg+phsJyC8TEEqiqIoimJW9/QquNeFhoYSHx9P3bp1q8RwSSklvdp04ymfgh0JmTlZbBPHWfbX8gLp2dnZnDlzBgcHB2rWrFlorqrBYGD2jB9Y/dsqjHoD1QKrM27i+9Q14fBhUzIYDMya/j1/L1+FzDHiF+jPO59MoE6dOhUdGgaDgdOnT2NpaUndunVNsiJrdHQ0Uz75mPNHj4AQtO7SldfHvVepP6s5OTmcOXMGKysr6tSpgxACvV7PzG+ns3HVn8gcI0YLDTn6HKyFFlcfT96Y8C7Ny7m3ePbMWRz76SANPAoO2d4Tf4DXp7+d36O6bMky5n25gBZObYlLi2H96b/QSA2WFpYkZMZT27Y+7eq1569zq6iprUOjmk3I1mWy8dIabO2tcPd0p8uDXXnr3bfLvFXMzUy+Cm5jD7n7j36lzm9d98crwM0bnc6RUs65+Zy8BuifRQ3BvekcW+AaUFtKGX/LsfnAYSnljLzfGwAbyN2IVQPcf1PjVDExtQquoiiKcjsl/V6iGqBV1KiXXsHjkiX+Tr75afujTtBqcGeGjhh2V2VNmvgF59YeoZ1nU3QaHdFpcWxLO8TPa5aUec9Qc/jio4lEbTpKN59G6DRawlLiWJ10igWrf/vP9TTp9Xqe79ubJ5ysaebtjgS2XgnjkKMH3y9YVNHh3ZUP/zeezD3n6OrTgIS4eC6GXWZj4kXevL8fmTnZLIk6wreL55fbHq6Qu6DRmCdG8aBnt/ztjbJyslmftIU/t/+FtfWNntEN6zfw44x5XAoOJjs6i5Z2bdBKLXqh50DqXhx9nfD188HOwY6Ia5GEXLjIw37daVitPkZp5FTcObSNrfn+x5kmi9/kDdAgd7lnRZ9S57eq/1Ox8ZSwAfoE8KyUsvct6Zbk9nA2klJG5aVNJ3eo7gohxOPAcCmleeY+KP/5e6uiKIpSemoblv+48Z9O4IA4x97oY5yPC2Fr9H7SqkueeeHZuyonPT2dLWv/ob1Xi/w5bp52bjQUASxb/Fsxuctfamoqu9dvoadvY3Sa3L1a/RzcaKXzZcXS3ys4OtPbvm0bjdDT3McDIQQaIegeUA157QoXLlyo6PBKLCEhgWPb99DDrxFaoSEhLp4GDj60ta/O3rBzuNs60sOxFovmzC/XuGrXrs0DTz7IhuitnIu9wMnYM6yP38w7H/+vQOMT4IEHH+D3P5cRUMOfAY0H0aBeA+rWr0ujeo3o13AADRrWY9X6lfz6+y+8Pu5VOgXeT8Nq9YHcuaCN3RsQevwyoaGh5XqNd61yrIL7JEUPv32I3N7PqJvSXgD+yPv5d+DuJgIriqIoilKu7uk5oCWVnJzMsl9/4ci/O/Dw9eOJwUNp0KBBhcbk6+vL8nUr2fD3ei4HX+bB1k/SsWNHtFrtXZUTFxeHk86+0FBRD1tXQs4HmzJkk4iNjcXVwq5QvL62zly+cMls9UZERPDzjws4e+I09YIa8NzQF/H19S0+YxldC72Cv3XhecQ1rC0IDw+vFMOOSyI6OhpPq9zVpY3SiAYQQuBj7cjptEQA/OzdOBYcUu6xvT72DR7u+wgb/96IjZ0Nn/T5Ai8vr9uen5KYgq1DweHPTtbOHA2/sQdpaPAVnDWFt4px1jgSHh6Ov7+/6S7ApCTCjKvgloQQwgnoDBT1NK2oeaHheedvA7oBVefJjKIoiqLcg1QDtBgpKSm88vTjdLfNYpSfKxHxp/l8xIsM/egLOnftVqGx2djY0G9g/zKV4eXlRbJMI8eYk98DCnA1LZIO9912scoK4+vrS6whnRyjIb8HFOBiWjQt2/YzS52XL19m5NNDaGtRk06O/lzZHMLQdc8x49e5Zh8u2rBxE37/7Rc635QmpeRUSiYvlMNWM6ZSvXp1wrNTMBiNaDUa0AiMUnIhPZYaeQ35cwnhNHm4YlYMrlu3bonnPPv6+5EQHo+LzY09hCNTI6jXtH7+701aNGXX4u3U5ca2MFJKog1xVeahgTkIIZYAXQB3IcQ14EPAAkBKOSvvtP7ARill2i15bYGewMu3FDsMmCaE0AGZwHCzXYCiKIqiKGWmhuAWY8XSJXS1yeTRur6421nT2NuVD1v788Okz4rcsL6qsbS05KXRw/g7Yidx6QnoDTkcjz5LpGMS/Qb0q+jwCrG0tOSZES+x5MpeotMSyTbo2R95nmDbdHr362uWOqd/+Q3dbRoQ5F4TB0tbgtwD6GHXkOmTvjFLfTdr3bo1aT7+LD8XTGp2NnHpGcw9dYEGnbvi7e1dfAGVhK2tLQMHP8Piy/uITU/G0cONf6LPcio9iqaeARyNusw+Gc2zL71Y0aEW663xb3IgfR8RKeEYjAauJoVyIucYr73zav45HTp0wCLAmsMxx8nKySIlK5XtUbvp3q8Hbm5uFRh9Ma7vA1raV3HFS/mUlNJHSmkhpawmpZwnpZx1U+MTKeUCKeWTReRNl1K6SSmTbknfJaVsKaVsKqVsK6U8dGteRVEURVEqD9UDWowj/+5gqK9rgTRHa0vsjVkkJSXh7OxcMYHdhcuXL/Pvrn9xcHSgcePG7N+3H61WS7fu3XB1deWJZ57Ez9+PRbMXEBtzho4DO/PxsJews7Or6NCL9PTzz1KtRnUWz11AXOwlOvbpyryhL5ltVdgLJ89yv0enAmk1nLzZcnqbWeq7mRCCb+f+yNJffubrNWuwsLTg0RGv0btfP7PXfV12djY7d+4kIiyMps2bExQUVKrVfQcPH0qNWjVZ8uNCkrOTqPVYexpFR/NL1EladWnH3BFfV+7GWZ4mTZowa8kP/DBtFvvO/UvDDo2YP2YeAQEB+edoNBrmLvqRXxb8zLrV67Cxt+H5V4fwaO9HS13v+fPnOXBwP25u7nTp3KXQHFWTMe1cTkVRFEVRlALUKrjF+GLCezS7doiWfjdWVzVKyehdISzZtAMLC4sy12FO33w1lXVL1+EtfYlPiedY9BGa+zbHydaJMBHGe5+Po+cDPSs6zErt6T6D6Gmsg6PVjQZ5SnY66+VZlv65ogIjM7/w8HBGPjcUv0xbXLDhsjEerxa1+Gr61Lueb6yUjpSSSV9OJDLyNF271SMyMpltWy/y2affUrt2bdOugtvIVe5b0qvU+S2a/mbSeJTKR62CqyiKotyOWgXXRB5/cQg/BycSn54F5DY+fz0VRsdH+lT6xufx48fZsGQjnd16UMe5Ph6Z3jzo9CiXYy7T2L0xPZx78MX7X5CWllZ8YfewF0YOZX3UYbIMegCyDXrWRx7m+RFDKjgy85s47gO6agLp5duM1r71GFTtPtIPX2XtqjUVHdo9Y8fOHWRmXWbK1Ofp3acdw4b3YuJn/fn8iw8qOjRFURRFUZS7phqgxahVqxavTprKxHMpvLP3MqN2hWDZ4RFGv/VORYdWrHVr1hFgEYhGaEhJTcECC+y0dtgJB+LS47DUWeIlvdi3b1+Z65JScvXqVcLDw00QeeXywIMPMOjtIfwa9y+/hG/nl7h/GfDGCzz86MMVHRqQuzJwSEgIRhOvXpqdnU3o2UsEOBeca9rGrQ5/r1ht0rqU29u6dT2PDWpbYNhzYKAPVlZ601dm5jmgiqIoiqIoag5oCbS7737a/rme9PR0rK2tq8zQQ51OhyHvS6HgxpdXozSgFbnXYMSITle2j8G5c+d4Z/RYZLLEiBE7Lzsmfz+F6tWrl6ncyuSxJwYxYNBAMjIysLGxQaOp+Gc3ycnJvP/Gm8Sev4iThSVR0sAbH35Axy6di89cAkIIJBIpZYHGj0Ea0Vmo/zrKi06nIzu7cGNTr88xQ21SzQFVFEVRFMWsKv5bdBUhhMDOzq7KND4B+gzsQ4jhIgajAQdHB/QimwR9AnptNi42LqRlpxGri6Nt27alriMzM5PRg0fTLKcJXV070921K4EJAYwcPNLkPXIVTaPRYGdnVykanwDjX3uD5jGpvFu/Ba/UCuJt//p8+977XL161STlW1hY0KBlU87EX8lPk1KyM/Ys/Z953CR1KMV76MF+/PrzvwX+PR0/HoKlpYt5KlQ9oIqiKIqimFHl+CatmEWdOnUY+vYQtiZu5GTSMcLtr7ElbSPVvP04mHCQXZk7+XLGJKysrEpdx5YtW/DMdsfZ2jk/zdPeE+tkKw4dUrshmEt0dDTxF4Np6eWbn2ZvacUDLl6sXva7yeqZ8PnHnHZO5Y/wfWyLOM6Cq9tp9Gh7evRUC1eVl5YtW9KoUReGDJ7FvB838dnEFUz7ZjsT3v/MDLXl9YCW9qUoiqIoilIMNY7uP+6pZ57k4Ucf4tChQzg4ONCwYUMOHTqEVquldevWWFpalqn8pIQkrIw3ypBSEhsTS8zVaF4Z/Aq9+/Xm9bGv4+TkVKLyoqOjmTrpG47sO4yltRWPP/8ETz/3dKXpdawsUlJScLIo/HfnYmXDmegYk9Xj7OzMohVLOHXqFFFRUTRs2BAfHx+Tla+UzJCXXqZvn4EcP36c1i1daNq0qfo3oSiKoihKlaQaoPcAJycnunXrlv97p06d7nD23Wl3fzsWTl1IkMzdGzLs6jUyU7KI0yfQz30goevDGHz4JZat+a3YuaZpaWkMfvwFGmYH0se5B1mGbP76bhXh18J4Z/z/TBbzf0FAQACRRj1p2dnY3fQQYV9CNA/3etGkdQkhCAoKIigoyKTlKnfH3d29wL9js7i+CJGiKIqiKIqZqEfoSpnUrFmTbv27sTVmGyGxIYTEX2Ff2kEa+DbC3tKeuq510UZr2b59e7FlrV65Gt8MdwJdAhBCYK2zoqNnGzat/ofk5ORyuJqqQ6vVMuaD95ly4Sj7wkM5HxfDzxdPo69dgy5du1Z0eEpVZpSlfymKoiiKohRD9YAqZTb2vbF0e6Abs6b9wOXwUDrX64a3fe7WHfEZ8YRFhjH508lkZGTw4IMP3rYn9NyJM3haehRIE0LgqnUiMjISR0dHs8R/8uRJ/ljyO9lZ2TzY7xHat29fYNXXyqprt24ELqnF6mXLCI+N48GeL9Cla1c1NLMKuHDhAn8sWUpKQiJdHn6Qbt27V5K/N7UKrqIoiqIo5lUZvvEoVZwQglatWvHJlxPx9PXMb3wGxwez/tRfeOW4UzspgF8+WsTwF4aTk1P09hH1mzQkKrvg/EUpJXE5iWabd/jLgkVMGPwGlrujcTmayqy3JvHxex+YpS5zqFGjBq+OHcuHX06ie48eVWqV5nvVX2vX8t5zQ3Dde5ag4ET+mjiVt14ZVTlWjVb7gCqKoiiKYmaqAaqYjK+vL006NOVw7CEyczLZHbKT++zb4mfvS03PQNp6tCH9XCobN2wsMn/f/n2JtI3jYnwIRmkkQ5/Jjui9PDTwYRwcHEweb1JSEot/+ImnqnWkjms1Apy86efXjlNbD3D27FmT16co2dnZzP5yCmNqt6KpZzUCXdx5KrAxOWcus2fPnooOT1EURVEUxexUA/QmUkpOnjzJ+vXruXLlSvEZlEI+//ozer7cgx2ZW5EYcXJ1ICAwIH9Ia4BNDbZt2FpkXltbW35athD7zu78mbyFnRyi/1tP8Mb/3jRLrMePHyfAwgOt5kavoRCCulovdu/81yx13itSUlLYvHkzO3fuJDs7u0JjOXfuHOvXr+fSpUslOj8zM5Pt27ezZcsW0tPTTRrLxYsXCbCww1pnUSC9hYMH/27aYtK6SkMAQhpL/VIURVEURSmOmgOaJzU1lbdeHoJjYhg1bQSrkg14NbuPDyd9XUnmZlUNOp2OoS8P4bEnBvJ4z0F4uxccOpuanYq/V8Bt83t4ePDxpE/MHGUuZ2dnUmVWofRUmYWru1u5xPBf9NfaP5k+8Rv8NZ4YMPC5NoFJM6fQtGnTco0jMzOT14ePJvFCJJ44EkkiPk0CmTxjKhYWFkXm2b9vHx+98R41hQsaIZhsmMhbn7xP957dTRKTs7MzCTmFP3MJ2Rm4enkUkaO8qcWEFEVRFEUxL9WyyvPtF5/STRPD/1pV5/FG1fj0vhpYn9vLimW/VXRoVZKzszMNWjbgfMKF/LTMnEzO5pzn8WeeqMDIbggKCiLTWcPlpMj8tLiMZC4QQ89ePSswsqorKiqK6R9PYZBnNzp6NaeLVysecbiPcWPG3nbur7l8/+132F7Mor9Pe9r7NGagT0dyjseycN5PRZ6fmZnJh2+M4ymPtvT0bUZ3n6Y8592Bye9/SmJiokli8vX1xa6GH8diwvLTkrIy2JEWTZ8BA0xSR5moOaCKoiiKopiZaoDmOfrvdrrW9AQpMRgMgOTx+t6sX760okMzq4yMDFJTU81S9udTvsCimRXrYjeyPWEH2zN3MX7y+wQEBJilvqJIKUlKSkKv1xc6JoTgu/mzOOEaz6/hO/gt4l82GM4wZd4M7Ozsyi3G/5J/Nmykvq46ltobPYyOVvZ45Dhy9OjRco1l69//0MqzXoG0dp4N+Wv5miLP37dvH4EaVxwsbfPTbHRWNNB5sX3bNpPFNWnGNI57WfPNxQPMCjnKrOhzvDf1Kzw9PU1Wh6IoiqIoSmWlhuDmkVISFxNNSkICWgEGBNYubhgMVhUdmlkkJSUx/p0JnDh4Gg0aPKu78cU3nxEYGGiyOuzs7Jg+azqJiYkkJydTrVq1ch3OvGXTFqZ++jXaLEmGMZsHBjzC62PfKBCDl5cXC5b9SkxMDHq9Hh8fnyqxBUtlZTAY0RTxXEuDKPdVXo1GWejvUiNuH4fBYEDIwn/3GkneQynTcHZ25rv5PxIfH09aWhp+fn6Va5i/msupKIqiKIoZVaJvPRVLZ+fIngtXqW5viZ+dJdVsLVh84By1mjSv6NDMYuSQ0SQcyKGdwwO0ceiJU0QNhj07wuSLrkDuF25/f/9y/ZJ96tQpvnn3c/rYtWWQVxee9e7BmT/2MuOb6UWe7+Hhga+vr2p8llGPXj04l3OVHOONBluaPoNwGU+zZs3KNZYOPTpzPPZigbSD0efo1eehIs9v164dlwyxZNw0R1NvyOF0TiSdu3QxeXyurq5Ur169cjU+AYxleCmKoiiKohSjkn3zqThZ6Wmsj8hk5sko1l9J4MsjEYRla4kOvXxX5SQkJBAVFYWUlXchjwsXLhATkkg1x5r5aS42bjikebBhfdFbpJREdHQ08fHxpgjxrqWnpxMWFpY/z3Dh7Pm0d2iErYU1ABqhoZN3c/5avsbkPXFGo5Hw8HCSk5NNWm5JJScnEx4eXik+c35+fjz/+hB+j9zE/sjj7I48wsq4HXw05VMsLS3LNZYxb73GVY8M1kXu52DEGf6M2EuSv4YhI4YVeb6trS3vfDaBReE72RF2gl1hJ/np2jZGjHsdN7d7ZFEqKXN7QEv7UhRFURRFKUaFDMEVQrwBDCV3yYsTwGDAFvgNCAAuA49LKRPyzh8HDAEMwKtSyg3F1WE0Gpk1ewa7/92GrZ0dffo8zkMPPnzbHi4LjEzv15FDYbGEJaXTL8CRuu6OvHM8qkTXFB8fz/jX3yLuUihWGi3Z9pZ8MPlLGjVqVKL85SkmJgZrWXiOozX2hF0Lv22+qKgovp/2Awf2HMTb15sRrw6jbdu2BAcH8/aYd0iJSseIEa8a7kyZ8TU+Pj63LctUDAYDX02cxI51W3DU2pJMBsPfHElkWCS1bGoVOFcjNOikhuzsbKytrU1S/769e/ls3MdYZWnIMGRRp0VDJn79Ofb29rfNc/DgQeZ/N5uIsHCatW7JsDEj8PX1LXBOUlIS82fN5d/N23ByduKpYYPp0bNHgXNSU1OZMHYcFw+fwVZnRaalkfe++JC27dqZ5NruRmJiInNnzuHfLTtxdnFm6P9GARIra2s6d+5sln1ci+Pg4MCi5Ys5ePAgwcHB1K1bl+bNm9+xl7tr92602NiSbdu2YcjJYVyXLiZpfBoMBn5fsoS/f1+O0WCkV/9+PPX8c7ddjbdCqVVwqywhRHVgEeBNbp/0HCnlNCGEKya8vyqKoihKWYjy7jURQvgBu4CGUsoMIcQy4G+gIRAvpZwkhHgXcJFS/k8I0RBYArQBfIFNQF0p5R0nZbm7u8pZc0bxyKOtSUpKZ9YPG/H2asGIl0cXef7LTw1iuLue6k43GmY7L0cTUr8jb7//wR2vSUrJ4EFP0iHDlgZuuY2u2PQUfow4wa/r1uDo6Fii96a8JCYm0qfbQNo59EKIG53gR5N28fmPH9CyZctCeWJjY3my7zN4pdfCz7EGyVmJnEk/xJsfj+a7yd9T39gSZxtXAKJTIwh3usiaDSvNPrxw2tdTOb1iLx28miGEINugZ3XETup3bop+TzQtPRvkn5uUlcpmeYIV61ebpO7w8HCG9HuWAZ7tsc9buOZE7CVS6lrw3Y8/FJln08ZNzBj/BQ+5N8XTzplzcVf5N+cyPy7/BS8vLyC3N/f5AU/QJMOJFp6BJGel83fUCXoOe4IXhr6UX9aYoa/gfCGbJm65De3U7HR+j97Nj6t+KdSgNaf09HSe6fckgakeNHCtRUp2GjvjjtBv1BM8/9IL5RZHZfbe62+gPXGGR6rXRCM0bLwWQnQNX6bNnVumYd9CiENSylamirNVXQe5b0aLUufXPbDDpPEod0cI4QP4SCkPCyEcgENAP+BFTHR/bdWqlTx48KCZr0RRFEWpikr6vaSihuDqABshhI7cns9woC+wMO/4QnJvmuSlL5VSZkkpQ4CL5N4s78jWzoL+A+7H0tICDw8nxr8/kF27Ntx2mOSY8R/y1Yko9oRGE52awYaLEfwWncMLL79S7MVcvHgRTXRSfuMTwN3WgVZWbqz7669i85c3Z2dnnh7yOIcStxOXFkVyZgInE/YT2LIaLVoU/eVz4fxFuKX5U82pJkJocLJ2pYVTJz794HPsUp3zG58AnvY+EK/jyJEjZr0OKSV/r1jL/Z5N8r/EW2ot6ODcmISoeILt4jgYfZrEzBQuxoeyNnY373w0zmT1L1/6O80tA/IbnwCN3Wtx5eQlYmNji4z3+6+m8rjffXjbu6IRGhq416CttjoL58zPP++vNWsJTLOhjXcddBotrjYOPOXfjmXzfiYzMxPIfSAQevJSfuMTwN7SlpZWAfzx23KTXWNJrPpjFd7JjjR2r4tOo8XF2pFHfDrw65xFZGdnl2sslVFwcDARh4/yRK362FtaYWthQb+adcm5dJmTJ09WdHiFSVn6l1KhpJQRUsrDeT+nAGcAP0x8f1UURVGUsij3BqiUMgyYDIQCEUCSlHIj4CWljMg7JwK4vieBH3D1piKu5aUVIoQYLoQ4KIQ4mJ1dcNsNjUZDUJAfly9fLjKuoKAgvl70GxfqdGBuoi1p9/dl1tIVeHgUvzl8QkICLtrCq+W66qyJi44pkJadnc3ff/3N5M+/YtWKlWRkZBRb/s3i4+NZtGARUyZNYd++faWezzhi1Mt8NvsDbFvmkFM3nqff7kfrdq2YPOkb9u3bV2g+4eH9R/Gyq0ZKSgqREZHEx8WjRUdOmkSTU3gkt6XR2uzzQY1GI8IIWo02Py3HmMPVxEiOHj7Mk4Ofoe5TbTnmFoHo6MH3v/1o0uGpsRHROFkVHmrroLUhKSmpULper8eQmomdRcHhv7Wd/Thx6EZj/fiBI9SyK/i502o0eOrsCQ/PHSKdlJSEg9amUB1OlvZEh5ds2LipHNt/BH9bb9LS04mKjCI2Ng4pwVljT0RERLnGUhmdO3eOetaFh7zXs7DhzOnTFRDRnag5oP8VQogAoDmwjzLeX2++t8bExNx6WFEURVHuSrnPARVCuJD71LUmkAj8LoR49k5Zikgr8lG7lHIOMAegZqB3oXMuXYq+47xEf39/3p7w4R1CKVqjRo24kJlIjtGA7qbG0LH0WEZ27JD/e3JyMsOefgGPRC3VLd3Y9vchFsycy5wlC0q0B+CxY8cYO/xNasrq2GntmLxiJz4tqvPtD9NKNdS1Xbt2tGvXjqNHj/LasLdwz66OlbBj0287qXtfDaZ9PzW/3IBaNdi78xhO0hMdFqSRQXR0FBo3SbJlHFLe2PJCSiPxIormzc27grBWq8Xdz5PYtETcbZ1Jy85g0ZGV+Goc6epeh93fr+SaXTpzlyw0yyIyHXt2ZunO2fg73fhMZeVkkyDSqVGjRqHzLSwsMFjkDhO+eZ/MsNRYAprd2P4moG4tQg9upYbTjc+ElJK4nLT8ByI1atQgQaSRlZONle7G4j5n067xRM8RJr3O4gTUrcmuv/6mltYHS2FJlswgLiaWCOto3N3dyzWWyqhatWr8pc8qlH7NoKeNv38FRHQHEjUH9D9ACGEPrABel1Im32GYd4nurzffW1u1aqU+IIqiKEqZVMQQ3B5AiJQyRkqpB/4A7gei8uavXJ/HEp13/jWg+k35q5E7ZPeOkpIyOXLkEgA5OQZ+XrQVP7/6JerRvFt2dnY8M+plfri4nzOx4VxOimVJyFHcWjYq0AibM2MWdZMd6OHTjHpu1eni05h2shrffP5VsXVIKfngrffp6tCeJh6NqOUaQBfP9kQdCmPTpk2ljl1Kybg33idI14Fazo2o5hRAU6f2XNh9lS1btuSf5+3ryfnU4xgxYKG1RKvRcTnzHFpLLR0fuZ9DCTuJTo0kMiWMffFbeXzwwHJpfLz32QesTzrAyZgL/HVuG02sfGnrXofWgUF09WlKi2xvpn31jVnq7ta9O5raTmwOP0RESizn467we8QOXhv/Njpd4Wc7QgieGfYia8MOkpmTOzQ1Nj2JrclnefGVGyuz9h80kEOGKK4k5f4T0Bty2BB2nPse6Jq/mI9Op+PV8WNZGrGDc3FXCE+J5Z/wQ4g6znTr3t0s13s7Pn4+HIg7SaYhG2utJZZaC86khpCYnoytrW3xBfzHBQUFkeXtwc6wKxilRErJvohrRNhb07Zt24oOT/mPEUJYkNv4/FVK+Udesknvr4qiKIpSFhXRAA0F2gkhbEXuY9nu5M5TWQNcX7HkBeD6SjFrgCeFEFZCiJpAHWB/cZVUr1aD33+7yDNPzeD5Z2eRlurH+Pc+NvnFXPfEs08zfu50IppV41SAPf0/fJMvpk4usMDIrk1baeZRcGXWeq7VOXGg+LmSUVFRkCpxtCq4mmhdu1psXFO6RQullGzfvp2U6AxsLAoOEfS1CGTd6hvlHtxzhPtrdSOYY+xL38CRrG34+wRgI+wYO+4t3p/xDo7tNHh0suHL+Z8yckzxc2dNoWHDhvy06he8+zUiJDuCFgENqFkrEK02tye6gVsNDu8u9uNSKlqtlh8WzKHf+BcIq2PAsms1vl08iwceeuC2eZ567hkeff15liYdZPbVzeywvsans7+hVq0bnwtXV1e++/lHjntn8d2VrcyJ3kP9J7vzzoT3CpT1wEMPMHXxbLRdq3O1jpE+41/kh5/m5F97WUgpOX/+PIcOHSp2mPiBXft5uF43DusvsDT6H5bHbcHZ3YU6HjW5du1amWOp6oQQTJ07h/jmQUw4c4T3Tx8mtH4gMxYuqHx7gEJu/1dpX0qFyrunzgPOSClvfvJm0vuroiiKopRFuQ/BlVLuE0IsBw4DOcARcof22APLhBBDyG2kDso7/1TeSrmn884fVdwKuJA73PHLSd+a5yJuo3HjxjRu3Pi2x61tbMg06LG7ec6iNKDRFd9gsLa2JttYeEGXzJxMHBxdi8hxZyEhIYwe9hqZMQauhV7jQuwFfKv54pC3fUi2IROHm4aA2tvbYWltw0MNB+WnSSnZm7oeCwsLOnToQIcOHQrVUx58fHx4feyb7Nq0DTsbhwJf6vVGAzozbnWh0+l4tHdvHu3du0TnCyF4/KknePypJ+54Xs2aNfl+wY/FllenTh3GffR+ieouqejoaF4bOgpjTCa2wpoIYxwj332NPv36Fnm+vYM90iKJp5sVPL4yags2NoXnqd6L7O3tGf/pxIoOo2TMOARXCDEfeBSIllIGFXG8C7mNo5C8pD+klJ/kHbsMpJC7XUjOzavsCSHGAKPJvUf8JaV8x2wXUbm1B54DTgghjualvQdMwoT3V0VRFEUpiwp5/C6l/FBKWV9KGSSlfC5vBb44KWV3KWWdvD/jbzr/MyllLSllPSnluoqI2RQGPvckWyKOF1jgZ2fkKR4eWPQX+5s5Ozvj3yCAkMQr+Wk5xhxOZJ5l0LN3bszcymg0MmrIq1RLaUgrly642rmTrk8h7Go4OTk5uQv5GM/xxDM3GpvPDnmaS5knMN600MiV5Au069QaS0vLImopf/2eHsT2qBMF3t8dkSfp8+SACoyq6nl75BsEpfrS26s93T1b8rhHV2Z9Op3g4OAizx/49CCOZp5Hb8jJTwtOvIZXbT81B7SKkeZfg2gB8GAx5+yUUjbLe31yy7Gueek3Nz67kruuQBMpZSNyF7m7J0kpd0kphZSyyU3v4d/3wv1VURRFqTrKvQf0Xjbw8ccIuXCJ+X9uwtfSmSh9MkEdWjJs5Mslyj/p2y9545XXuRCyHTthQ4yM5+X/vULDhg3vKo6TJ09CsgXO9rkL83Ss1YutF/4mPT2DuIjL6NyMvPr+SBo0uLGH5n333cezowexYPYvOAl3MmQqgU39+fDTO++RWp6efu4ZLl8MZsE/2/CxcCJSn0TzrvfxwpDBFR1alREeHk5qWDw1vG705FtqLWhuXYuVy/7grXffLpSnbt26DH9vFDO//A4vrSvpxkzsqjvz7XfTyzN0xVTMuJ2KlHJH3uqspvQKMElKmZVXR3Qx5yuKoiiKUoHErdtt/FdU5s2y4+PjuXLlCn5+fiVa/fZWV65cISEhgXr16t3VEMf4+HgmfvApOzbtIvJaNDVd69HKvwNWutxtQU5EHqR5/zq8/8H425abmprKhQsX8PT0xM+vyN1wKtz197d69eqqB66E0tLSmPzpl2xdv4mIy9eo51qTh+p2wsEqd25wcMI1RAd3Jnz20W3LyMzM5OzZszg7OxMQEFA+gZuRXq9nxjfT2LT6b6TBQI36tXn3kw+KXOHYHPbu28OcOdPJyEhGCAsGDXqOfn0HFJhXXtINn0uqZW17uW/y7acRFMei/95i48lrgP55hyG4K8hdHCcceFtKeSrvWAiQQO5s09l5K7OSN9R0Nbk9q5l5eQ6U+iKUO6rM91ZFURSlYpX0e4nqAa0Arq6uuLre/bzN62rUqHHXX4INBgODnx6CQ7Qv3TwGcCH+IkmpCWw5/ycPNhgISDKsExk85MU7Nmrt7e3Nvr1KWZX1/b0XvTpsJD7XNIys+RCXsi8Sn5XKL0dXM7z1E2iEhtMZV3ijz3N3LMPa2ppmzZqVT8Dl4ON330e/P4SRfh3QarRciYpmzHND+eXPFTg6Opq17pMnTzJ79hd8PukpPD2dSUvL5MsvVgLQv99As9ZN2bbzdBdC3Nw6mXO9oVhCh4EaUspUIcTDwCpyF8YBaC+lDBdCeAL/CCHOSil3kHsfcwHaAa3JnesYKP+rT1cVRVEUpYqrhEswKuawZ88eDFE6qjkGoNVq8a3mg7POFX1GDscj93MgeSu9n36Q2rVrV3SoSjk7e/Ys6SGxNPeojVZo8POvhquVPW7YsvHSbtZE7KROl8a0adOmokMtN/Hx8Zz69yBdfRuhzVs0rIaTJ41xY83KVWav/9fF83lr7CN4ejoDYGdnzf/G9Wf58l/MW3FZVsDNbe7FSilb3fS6m8YnUspkKWVq3s9/AxZCCPe838Pz/owGVgLXP5DXyF2sSEop95PbhFZDHxRFURSlklI9oPeIq6FXsdLf2GrF0dGROM9IMq+kEma8yPDBQ3hlZOGtU6Kioljw40KOHzlJg0b1GDz8xUo79Fa5OwaDgbWr1rBg1o9EXAnhlIUPDd0DsLO1JbBOLcIvJZMUZM+EcWNp3LhxgaGfFU1KyZbNm1n16+/kGHJ4+LG+PPLooybb1iQyMhJPS/tC6T7WToReDCkih2mFXQulTp1uBdLs7KzJ3TrZzCqw31AI4Q1ESSmlEKINuQ9J44QQdoBGSpmS93Mv4PoCRauAbsA2IURdwBKILf/oFUVRFEUpCdUDeo9oFNSIVIv8hQ85cm0vZ66eoK5lc+53fpCN83cxcujoAivIhoaG8mTfZzm2PBjPyNqcXRvGM32f58KFCxVxCYoJSSl5c+SrbJi8iIdEHbrb1+PgxWNsuLQPAJ1WS4J9Dq+PfYMmTZpUqsYnwOTPv2TR+G9oEmVL63gX/vxiHuPeGGuy8gMCAriWnVRg1WeAixkxNGnTwmT13E6Dho3Zv/9cgbTo6ESsrR1uk6NqEEIsAfYA9YQQ14QQQ4QQI4QQI/JOeQw4KYQ4BkwHnswbSusF7MpL30/uVivr8/LMBwKFECeBpcALavitoiiKolReqgf0HtG4cWMCmvly6tAhqtkGcin6HM2sOmDrZIObvTtuuHPk2C4OHjxI69atAZj29XQC9A3xca4OgL9TILZpdnz16WTmLpxdkZejlNHRo0dJOHGFgdXaAWB0y8LTwpHfIw7Q3LsuZ1OuYV/Pq1LO942IiODfNf/wkn/n/Ibxo7atWLJ/N2fOnCmwenNp2dra0u/5J1mycCUPeAbhYGnDwZhLRLkIej3wQJnLL87zzw3lrbdfRkpJmzb1uHAhnG8m/82IEePMXjdG8z1skFI+VczxGcCMItKDgaa3yZMNPGuSABVFURRFMTvVA3qPEEIwY853PDC0I9vj12AjbHH1dsavmm/+OS7Sm317biweefzwCbwdqhUox93Oi0vnit4PUskdurl08VJWrFhBYmJiRYdzW4cPHCJQe2OanK+fLy4+7njZOLEi+SCthj3M9DkzK13PJ8CJEycI1LkVii1Q68qRQ4dNVs+wkSN44dOxbLOLYXH6CXwfa8/cxQvLZd9bPz8/pkyeze5d6bw8dBGLfznH2299Tts27cxedxnngCqKoiiKotyR6gG9h6xZtYZlP/1BdVGXy8ZLxEbHYWlpmb+iZwYpVKt+o0Hq6u5KWkwK9lY3VvzM1Gdg72BXqGwFfvt1CQu+nUt9rS8GIZn71QzenfQhXbp2qejQCvH29eG4TL+RIATOLi7YVnPl83lTqF+/fsUFVwwvLy8SySyUHi8z8PLxNmldPXr2oEfPHiYts6T8/Px493/lvM+uxKw9oIqiKIqiKKoH9B4RExPD9C9mcp9jT1r63o+NrQ0pOUmEXQvHYDQSlx5Dil0svR7olZ9n+KtDOZm6H70hG4AcYw4nUvbx0sjBFXUZlVZERAQLv53L037daOPbiPt8gnjcszNfjPuEjIyMig6vkO49uhOiS+JqcjSQOyf0VOwVdL7O1KtXr4Kju7MmTZqQ6WbJ2bir+WmXk6IIt86kU6dOFRjZf4M0lv6lKIqiKIpSHNUAvUds374dtxw/tJrcTu/udR8l1SaWg2lb2Rz5B6nVw/nx19nY2trm5+nevTujPxjOEcN29qVs4mD2Zl5480n69utjkpjS09MJDg4mLS3NJOVVpG1btlJf54cub8sOABsLK/w1bhw6dKgCIyuatbU1MxbN5YhrInNDtzDn6hbiGtgy7ceih90mJSURHBxMdnZ2BURbkBCCGQvmcK2WjllXtjD7ylZOuKcx8+cfsbCwqOjwFEVRFEVRlDtQQ3DvETqdDnnTDvNWOmu61nmY4/F7eW3yy/Ts2bPIfAMeG0C/Af3IyMjAxsbGJNtcSCmZOf17Vv/6By5aJxIMyTw08GFeG/uGybbRKG9arQ4jhbuAjEh0usr5z8zf3595SxaRkZGBVqstcm5jdnY2H42bwPFdh3DW2RFnTGHoGyMZ+PhjFRDxDa6urkybM5Ps7GyMRiPW1tYVGs9/ilRDcBVFURRFMZ+q+W1fuWtdu3YlziKcrJwbc+fS9WmkWScWO2xRo9FgZ2dnssbhyhUr2bboH/p79KKb23309+jJ/t//ZckvS0xSfkXo0asHZ3LCyMy50UOYlJVKhCaJli1bVmBkxbOxsbntwjqTP/+S9L1hPOfXnd5e7XjWqyu/fDWn0vTqWlpaqsanKUlRtpeiKIqiKEoxVAP0HuHk5MQnkz/gUOZWTiUe5GTSAY7l7OTr7ydhZWVVrrEsnvcL97u3QCNyP34aoeE+9xYsW1h1G6Curq6M/ex9lkZvY1vkETZHHmJN0j6++H5ylR0WajAY2L5uC209b2xrotPo6OjciF/mLqi4wBSzkkZR6peiKIqiKEpxKufYwAqyfetWfpn9LakJcXhVD2D4m+/RsGHDig7rtrZs2cKsqbNIjEvEP9Cft8a/dcc9ELt07cK6HW05cOAAWq2W1q1bY2lpiZSSZUt/55f5i0lPTadxiyDeGf82vr6+ty2rLNJS07B2LNjotdRakJlceGXTqqRHrx7c3+F+DhzI3crm2MEjjH/tfxgNko7dOzHqzTH5Kw5XBTk5OejQ5D8ouM7Ryp6EuJAKiuru7Nu7lx8mTycuKgYrB1sstDpSE5JwcnXmpVdfoVuP7hUdYuWjejIVRVEURTEj1QOaZ9PG9fz25duMa5DB3AdceNEjjE/HvMCFCxcqOrQirftzHV+99SWNUhvyoGMvPK64Mfq50QQH33mPThsbGzp16kT79u3zh13O/O4H5n+xmNoZLWll2YP4PXqeHzSYhIQEs8Te+v42XEq4UiDtctI1Grcscp/5KsXW1pbOnTuzaukKTv++n372HRnk0oXYfy4x/NkhGI1VZ6lQKysrnLzdiE1PLJB+Ij6YLg9W/obb7n9388WY9+iS5UcfhyDCDp4h8JyeF1xa8xCBzHpvEn//+VdFh6koiqIoinJPUQ3QPAu/n8K4Du54OuT2zNX2sGdMc1sW/jCtgiMr2sypM+nk1hEHK3sAPOw8aGrZmNnTZ99VOZmZmSxbtJxmLvdhpbNGCIGvoz/Oab4sW/q7OUJn9FtjOGN5mUMxJwhPieJo7CmOay7w5ri3zFJfebty5Qohhy9wn1dTLLQ6NEJDE/d6WEUb2bVrV0WHd1fe/+Ij/kw8wNHo81xLjmJ75FGiPbN5/KknKjq0Ys2aPI1+3i1xs3FkS/ARHnFrTF17L2Iio3G2tmeQX2vmTZtZ0WFWLtf3AS3tS1EURVEUpRhqCG6enLRkXGw9C6Q18nbg++1nKyii25NSkp6UhrV7wcVXvO292XN6712VFRMTg61wQNwyzNLNypPTx8+UOdaieHp68tufv7Pqj1WcPnqStkFd+fKxAVVqeOp1BoOBfzb+w9b1m3H1cGPQ048TGhqKp8a50LkewpmL5y5Uqb0qGzZsyMI1S1nx2+9cC7lKr/ZP8tCjD5f7vOHSSIiJw803CIDo1Hge8qyLTqMlOzsVAFsLa7KS05FSFrn1TGno9Xo2rl/P/u2bcfHwYsBTz+Dv72+SssuLVENwFUVRFEUxI9UAzWNh70R8ejautjdWAz0Rnkxg/WYVF9RtCCGwdbIjQ5+BjYVNfnpESgT12tW/q7I8PT1JJxWjNBaY6xefFU2vZu1NFvOt7O3tefb5Z+F5s1VhdgaDgZEvjSD9TAJ17GoQp7/CyFXDee61wcQYEwudHy0T6Vu/bvkHWkaenp68MmZURYdx19y8PIjLSMbNxhFvBzeuZMRR3cYVS6vcf+Pp+kysHG1N2vgc89Lz1EwOo6ePI9FRJ3n3+dWM+vRr2nfoaJI6zE+tZqsoiqIoinmpIbh5Xhz9Np/tjCUiKXchnPPRqcw4lsELI16r4MiKNmbsGHbE7yI5KxmAqNQojutP8PKrL99VOVZWVjz5wiCOJuwmMycDKSVhSVdItI/g8ScHmSP0/4xtW7eSdiaBzt5t8HXwoq5rTR717MwvsxcS2Ko+/0YdJdugx2A0cDTmLHpvLe3bm69RrxT0ytjX+CPyIDHpiXQNbM5fsSc4lxKBh7cnCZkpLLu2n2FvjjFZfRvW/U1gchiDG1enrrsTHWp48XGr6kz75IMqNfdXURRFURTFnFQPaJ5uPXphZW3LlB++ISkuGr+adflo5v+oVatWRYdWgNFo5ODBg6CBkR+OZNnCZcTFxFKzTiA/jJtFzZo177rMEaNexsvHi0VzfiYlJY2WXZrz1Ts/4eTkBMClS5e4cOECNWvWpF69eqa+pCpr24at1LatXiDNWmeFo9GW4a+OYMfW7fz5+xpycvR0ebQbE18dZbK9VP+Lzp07R0hICLVr16Z27dplLq9tu3Z8MPMrZk35jujwSGre34RrWi2nYg/h6uHGmMkf0Kmz6YZD79m8kd5+TgXSnKwt8dTkEBkZabZVpU1KorZTURRFURTFrFQD9CbtO3SgfYcOFR3GbYWHhzPyxRHYJFliI60IN0bR57n+jHytbMMjhRAMGNifAQP7F0jX6/W8PupNzh8MwTbHmQxdMtUaeTJz7gysra1vU9q9w93LgytZpwqlpxkycHNzY9iI4QwbMbwCIqtaMjMzeWPEGJLPh+MtHFhgTMKrcSBfz5iav1JzabVq3Zofly4yUaR35uLhReyFCwS6FkxPyMrB3t6+XGIwCTUEV1EURVEUM1LdMVXI/14dS+PMOnRyb0trj2b09uzJ+kV/cejQIbPUN2/OfK7ui6O5Q2fquTSlmUNHEo7r+e7bGWapr6p57KlBnMoJJl2fkZ92Lj4Y/0Y1cXd3r8DIqpYfpn+P48U0Hve7j06+QTxVrT2aU7H8NHdeRYd2VwY8/SxLriSRmqXPT9scEkWNZq2r1gJbUpT+pSiKoiiKUgzVADWh9PR0NmzYwKpVq4iOjjZp2XFxcSRcjcfXwTs/TSM0BNnWY+WSFUgpOX78OMuXL+fQoUNIKctc5+rlf1LLIahAWqBjA9av/afQufHx8axZs4a///6blJSUMtd9N4xGI7t372b58uWcO3eu3Or18/NjwjcfszZxO0sv/Mmii6tIqwtfTvu63GLIzs5my5YtrFixgrCwsHKr97pz586xfPlydu/eXep5jlv+2kAbr4JDu9t51mPtspXk5OSYIkyTk1Kyf/9+Fi5cyNGjR5FSEhgYyLAPPufdo9F8eugqb+0O4bh7fd7/bFJFh3t31DYsiqIoiqKYkRqCayKHDx1i4htv0NrGBmsBiyd/zYDhL/Pk86ZZ5tVgMFDU1zsNgqysbIY8O5Toc3HYZzuRbpGCbQ1rflw0p0xD/4xGA5pbVggVQiCNBRu3q1euZvIn03DO8QGMTLKazKdTPjLp/LrbiYuL4+XnhuKQIHA22vKbmE+NVvX4ctpktFqt2es/vP8QOr0WFwsnjDoIOR9CZGQkDg4OZq/7woULvD5kFNX0TtgYLVgoZ9HzyUcZ86b5F84yGAz877W3uXzgPH7ShURNOt+4SGb/8iNubm53VZY0FtwG5WjkRTZc2E+GQU/vjr145PF+jHxtdKWZP5uSkkLf7j1JCAnF08KKqTlZ+DWozx8b19GlW3c6denKtWvXcHR0xNnZuaLDVRRFURRFqVQqxze6Ki4nJ4fP3n6bcXVq8XidWvSpXYtPmjRmzZw5hIaGmqQOT09PbD3tiU2Py0+TUnI6/QIGrST9jJ42zh1o6NmYVi73Yxlqy9TJ35apzof7PkhwcsF9UK8kX6Brr875v8fFxTH5k2m0su9Ofdem1HdtTjOrLkx4+2MyMjJuLdLkPn3/Y5qke9PLpzVt/BoxwLcjiQdCWbniD7PXffToUTYtWU8fr+509r+Prr730cmqBe+MetskPdB3IqXk3dFv87Btc7r5tOA+v8Y85deF7UvWcfToUbPWDfDH8hXE7w+lv09H2vgG0cu7DY3Sffh0/Md3XVaHnl04Gn0JgEsJ4Wy/cIT+Lq0ZXu9hBvt05diybcybNdfUl1BqY4YOwysqkW8atWdC/bZMadAe7aVQxr01FgCNRoO/v3+VbHxKKcr0UhRFURRFKY5qgJrAiRMnqKXT4WpzY09OnUZDd1cX/vn7b5PV88W0L9ltOMKemEMcjTzJXzFbaNW7HWePnaWuc8MC5wY61WX7xh1lqu/lkcOxqyc5nrKbi3GnOJGyFxGQyhtjb/Swbd26FdccP3Qai/w0K501jjke7N+/v0z1F8doNHLq0HFquxRciba1e33WLltl1roB1q5YQ0Pr2gX2T3W1cUEkG7l69apZ67569Sq6FAPuts75aRqhobldIH/9scasdQP8uWw1rd0aFEir41ydk4eO3/VQ3NFvvkaIexZrwg/wx+kdtLGvhcbaAg8PD7RCQ3fvpqz89XdThl8mB7bt4MUaDbHQ5PawW2m1DAloxOZVqys4MtNQDVBFURRFUcxJDcE1AY1GU2j9jf1hYfx27BQZIVdJiI3npZEjcHV1LbqAEgoMDGT1prXs2LGDuLg4WrduTWBgII90612ox624Hjij0ciqlav59aclZGVl8Ujfh3hxyAvY3NSItra25uffFnLkyBHOnz9PYGAgrVq1KjAUUgiBpHBdUhoLDKssjezsbH5Z8DN//7EWnYUF/Z8eyKAnHi92KKaUslyGa2o0GqS80djSG3LYf+0Ix8NOMPqlkTw79Hkee/wxs8QihMBYxN+xUUp0Jq5v//79zPtuDpHhEbRo14rho0cgNKLIz1hp/sbt7e1ZuHwxBw8e5J1XXqe2W21cHG5sZ2Kh0SH1BoxGY6UYhquREludRYE0ZwsrDHr9bXJUMcaKf48VRVEURfnvUt80TCAoKIgQg5GYtHQA1l24xJoj5+njWpePG3bFYvtxhj3+tEkW57G0tKRHjx488cQTBAYGAtBnUG/OJRbcDuRi4jl6PNz9tuV89vEXzP54ET7xDamd0YYNc/9l8LNDC/VeCSFo0aIFTz75JG3atCnUAOjatSvxujD0huz8tEx9OimWcbRt27bU1ymlZNSQVzgwfzMPaFvSxRDE+m+XM2Hs+PxzNBoNTdu24Fz8lQJ598Wdoe8TA0pdd0n1eawvpzIvYpRGpJQsP7mWlNgk+rh1p6u2NasnL+PDcR+Ype5q1aohXK2ISovPTzNKI4fTL9H7sX4mq2fj+o18OnI89SNdecy+PTk7Ihn6+At0eag7++PPFDj3bMJlmrZrUapGokajoU2bNvR5fABXMgou4BWfkYyrj0elaHwCeNWowemkuAJph+IjqdesaQVFpCiKoiiKUnVUjm90VZxWq+XDb7/l65DLLDx3np+PnOQhj7rUqhaArZU1bX1q0lLa88ey5Wapf/CQF3Fv6cSexO2cij3GvoSd6OpJXn1zTJHnR0dHs2ntNho7t8PGwhYLrQV1nBuTHJzJv//+e1d1u7q6MuGLcRzO2MLZhCOcSTzMcf0Ovpz+GVZWVqW+piNHjpB2IZb7vJpipbPE1sKart6tObnrSIF5te99MoHzzvH8HbmPPREn+D1sO74d69Onf99S111SjRs3pveQAayO2sS6kK1kZ2RT274GNQNqYmthQ0ev1hzZesAsq9MKIfhyxhT+yT7BP5GH2BVxnF/CtvDIS48RFBRUfAElIKXkuy+n0turPZ52rmiEhvpuNWks/YmJjMGvUz2WR2xjb+QJ1kXt5ZJLPOMnlq3B/eKwlzhuEcXBqHMkZKZwLi6UVXEHePujcSa5JlOYOn8usyMvsCU6lCupiayPvMyvCVeZMvuHig7NJKQs/UtRFEVRFKU4agiuiTRu3JilGzeydu1aznw2hVp166LT3nh76zl6sPfAQRgy2OR1W1paMvPH7zl37hwXL16kRo0aNGrU6LZDYC9evIgT7oWOOxjcOHb4OB07dryr+h94sBcenu78OHseFpYWjBz9MfXq1Ss+4x2cPnkKbwoPWfYRLpw/fx5/f38AXFxcWLxqGUeOHCEiIoJGjRoREBBQprrvxssjX6b/Y/2Z9PkkPLdEUrt6bch7X4UQeAo3zp8/j5+fn8nrrlmzJiv/+ZN9+/aRlJREq1at8PT0NFn5mZmZyIwcbJ2sC6QHOvmx69AxFq34lcuXL3Pq1Cl8fHxo3rx5mYddu7q6smjlUpb8vJgDew9QvUUNvh8yrlz/TovTuHFj1u7fzfSvvmbFqdMEde3C+rffwsPDo6JDKzuJmsupKIqiKIpZqQaoCVlaWvLII4+waMp0NJqCW4CEpiZQo2M7s9Zfr169EjX8qlWrRppIKpSeJpKpWbvmXdc76/vZ/DZ3BV6yJlJkM2znKF59bwSPPf7YXZd1nX9ADTZReMhyPKlUq1atQNr1YcIVxdPTk0f7PMpPe2blNz6vSySlULympNPpaN++vVnKtrKywqCV5Bhz0Glu/FcRmRpLQFDu5yQgIMDkjUMnJydGjH4FRr9i0nJNyc/Pjy+nfVvRYZiBUHNAFUVRFEUxK/VNw8Ssra3p3r83K66cJNuQA0Bocjzb0iN5/NmnKzi6XP7+/tRpGsDFpFP58xcjUq5icE2lZ88ed1VWWFgYS+Yup7VTd2q41CbAuS5tHLszfdL3JCcnlzrG9u3bk+pi4Fx8CFJKjNLI0ZizONZyp379+qUu11w6depEqnMWlxIu58d7IvYMbnU9qVOnTkWHVyoajYYnX3qWDRH7ycrJneMbm57I7vQzvPjySxUcnWI2UpT+pSiKoiiKUgzVADWD0W+9SaNn+jI17AhfXNzNFvtMJi+Ya9LhkWX17cxvaD2gPoeyNrE/fQNu7XQs/G0+lpaWd1XOzh07cc3xQdy0FYlWo8PZ6M2BAwdKHZ9Wq2XOr/MwtHTk16gNLIn+B5fuAUyf+z2QOz/x4sWLHDx4kPT09FLXYyo6nY4fF89HtrRlecwG/oj7B+9eNZk2+7uKDq1Mnn/pBfq89iSr0vbwc8RGDjmE8uWPU/MXwFIURVEURVGUuyGK266jqmrVqpU8ePBgRYfxn7dmzRrmvb+U+q7NC6SfTN7P+9+/yX333WfyOmNjY3l92ChyolJx0NhwLSeOoW+OYmAZhvwqSlUkhDgkpWxlqvJaVHeW21/rVOr8jmPX3jEeIcR84FEgWkpZaLUsIURfYCJgBHKA16WUu4QQ1sAOwIrcqSPLpZQf5uWZCPTNyxMNvCilDC/1RSh3pO6tiqIoyu2U9HuJ6gFVyqR79+4kWUeRrk/LT0vOTCDbLoXWrVubpc53X32boCR3Bnq3p5dnC5737srCr2dx7tw5s9SnKPcMSe4c0NK+ircAePAOxzcDTaWUzYCXgB/z0rOAblLKpkAz4EEhxPVJ9V9LKZvk5fkTMM/eR4qiKIqimIRqgCplYmdnx5QfvuIMeziRuofjqbu5bHOMmfO/Q6cz/RpXMTExxIVEUMv5xqqyOo2ONra1+GPJ7yavT1HuJRKBlKV/FVu+lDuA+DscT5U3huXYkdskRuZKzUu3yHtdP3bzZPP8PIqiKIqiVE5qFVylzFq2bMnGnes4ffo0Op2OunXrotGY59lGZmYmlqLwx9ZaZ0VialoROcwrODiYzyd8SuiFEIRWw8MDezPq9dFlbnxfunSJzyd8ytWLlxFaDY881oeRr40yS6O+qtPr9Xw/dTobV60Fg6RGvdr875MJlWrrlqqkordhEUL0B74APIFHbkrXAoeA2sD3Usp9Nx37DHgeSAK6lmvAiqIoiqLcFdUDqpiERqMhKCiI+vXrm63xCblbyGTZQlJWaoH042mX6dn7TiP7TC8+Pp6Rzw4nMMKVJ70f5DG37pxcvo/PPphY5nJHPfcytSLdeNL7QQa6def4sj18/uGnJor8v2XiexOI/HMvY6p34vXArrSItWbMc0NJSiq81ZBidu5CiIM3vYbfbQFSypVSyvpAP3Lng15PN+QNs60GtBFCBN10bLyUsjrwKzC6rBehKIqiKIr5qAaoUqUIIfho8mesiNvL3shTnIoJZmXYv/i0q0vHjh3LNZY/lq2grqyGt70HAFqNlnYeTdmz6d8ybUGz/LffqUf1/HJ1Gi33eTZj9z+7ylTuf1F8fDwndu2nm18jtHkPPmo4edBM486aP1ZVbHBVkQRpLP0LiJVStrrpNafUoeQO160lhHC/JT0R2EbRc0kXAwNLW6eiKIqiKOanxvMpVU6zZs1YvG4Ff6/9i7iYOMZ2G0WzZs0QonyHDl6+GIKTzoG9V49wMfYythbWtKzWBGedA9HR0Tg6Opaq3JALwXhYuRRIE0LgrLMnNja21OWaU3p6OsuXLmPnxi24erjx9NAXadq0qdnrjYqKwsPSoVC6r40zVy4Fm73+/yRZcc8lhRC1gUtSSimEaAFYAnFCCA9AL6VMFELYAD2AL/Py1JFSXsgrog9wtiJiVxRFURSlZFQDVKmSXFxceOb5Zys0hqCWjZm44AOaWteik11T0gwZbDm7kzRnI9WqVSt1uS3atuSf3avwdfDKTzMYDSQYU/Dz87tDzoqRlZXFsKdfoFqchq6u/iReSOOTYW/ywrgx9Onfz6x116hRg/DsJIxSornpAcSFtBi6tn7YrHX/Nwkw4xxQIcQSoAu5Q3WvAR+Su6AQUspZ5PZePi+E0AMZwBN5jVEfYGHePFANsExK+WdesZOEEPXI3YblCjDCbBegKIqiKEqZqQaoopSSpaUV1Sw9aGAXgI3OGhuNJR2dmrFRHi7TPNhH+/RmyU+LORZzhoautUnJTuPf+KM8M+p5rKysTHgFpvH3n3/hFSvp7NcQAGdre56zd2X25O94uPejZl04ydbWlgEvPsPin5bzoFcjHCxtOBgTTISz5IGHyndOsFI8KeVTxRz/kryezVvSjwPNC+cAKaUacqsoiqIoVYhqgFZhOTk57Nq1i9AroTRp2oSmTZsWOww1OjqarVu2otVq6da9G66uriaJJTo6mi2bt6Kz0NKtW9nKDQkJYfe/u3F0cqR79+7Y2tqaJEZTO7znIO1rt8I6W0dycgpanRZfHz/qZCUSGhpK7dq1S1WujY0NC5YtYuG8BWzcsAUXDxdeH/8/Onfpcsd8er2eHTt2EB4WRrPmzQkKCiqXYckHduymnoN3gTRLrQXuGlvCwsKoUaOGWet/6eVh+AfW5Lf5i0hOSqLzgB6MfWkwlpaWZq33v6qiV8FVFEVRFOW/TTVAq6jY2FhefGoIxFphnWPPYos/qNbYkx9+/B4LC4si86z4fQXTPv8e95xqSIxM/+J7JnzxHj0f6FmmWJYt/Z0ZX/6QW64wMv3zmXww6T169OpxV+VIKZn61TdsXr6RGviQSTbffT6NqT9Oo1GjRmWK0RyqBVTn/O4D1PCqh6fXjeGyyWmpuLm5lalsR0dHxrzxKmPeeLVE50dERPDKc0PxSLPFGVv+ksvwaV6TyTO+RavVlimW4vjWqE7sscNUd/TIT5NSkpiTjouLyx1ymk6Pnj3o0fPuPm9KYVLmLyakKIqiKIpiFmoV3Crq4/cn4hJXnUZOranl1oCmju2JOpLCkl+XFnl+TEwM0z6fSVuHntR1a0w9t6a0suvBxPFfkJZW+v0zo6OjmfHlDzfKdW1KK7vuTHzvc9LT0++qrKNHj7L998309uxGU6+GtPVqRg+7drw75h1u7E1feQx4fCCnDJdJyMjd7sMojRyKOU2jtk3LreF13cfvTqCtMZBuPi1o4VOfvr7tSTkczupyWAl24FOPsyczlLiM3BV6jdLIrsizNOnQulIumKTcmURT6peiKIqiKEpx1DeGCqbX6wkODiYxMbHEeaSUHN1/HB8H/wLptRwbsHr52iLzbN++HdccX7SaG53ellpLXHK82LdvX5F5SmL79u245fgVKtfJ4HnX5f696i/qW9UsMGzUydoRqwwdISEhJS4nPT2d4ODgMjWsS8LT05PJP05jj+V5lkVuYknUP7h3q8nErz8za7230uv1XD5ziQBn3wLpLd3q8efy1aUuNzY2lpCQEIzGO3eJ+fj48Nnsb/nLEMycKzuYGbodh64NmfDZJ6Wu+2YGg4GQkBDi4uJMUl5FCg8P59q1a5Xygcp10ihK/VIURVEURSmOGoJbgVavXMX3X83AWdiTkpNGULumTPzqU6ytrYvPXMR3PYM03nbBF51OB6JwQ8Iobp+nJHQ6HVIU/jItkXddroWlBYYixv8ZjIYSlSWl5Ptvv+PPpSvxsHAiVp/MA489wqtvvVGmRYHupHHjxvz253LS09OxsLC47fBnc9JoNEhyr//mxrvBaChVPElJSbz72ttEnL2CvdaaJF0W73wyno6dO902T9OmTVn610rS09OxtLQ02cJDmzdt5tuPv8ANa1IMmfg1qsNn33yFg0PhrVcqs9DQUD76+F0cHCRarYb4OD3jx39GnTp1Kjo0RVEURVGUcqV6QCvI8ePHmf3pTPq6dKWH2/309+pJ+oFYvvjo82LzCiHo2K09V5Iv5KdJKbmYcoInnhtUZJ6uXbsSbxFBVk5mflqGPo0Uy1jatm1b6uvo1q0b8RbhBcpN16eRahl31+X2fawfp7MuYTAa8tOi0mLRuVtRvXr1YvP/sXwF+5Zs4gXf7jzq2YbnfbtxdPlOlv665K7iKA1bW9sKaXwCaLVaGrdpxtn4K/lpUkr2xJ+h/zNFfx7u5N1X38L3iuA5v870927L405t+GLsR4SFhRWb19bW1mSNz+DgYKaP/5Qhnm14yq81w/074hucyQdvv2uS8suLwWBg3LjXeOfdbnzz7fN8PeVZPvzkESZ88CZZWVkVHd4t8rZhKe1LURRFURSlGKoBWkF+mfczrewbYaG90Whp4taAPVt2kZ2dXWz+9z58F12dLI6m7ORswhEOpmym5UMN6de/b5HnOzk58cnkDzictY3TSYc4lXSA4zm7mPz9l2Xa2sPJyYmPv55QoNwTObuYPPPLu16FtF69ejzz2vOsivmHvTFH2Ba7j4Oa00z5YWqJVnP9bf6vdPNqhkbkfqw1QkNXr6YsX1T0vNj/kgmffUSwezJ/Ru5hZ+QxloZvJeiRtjzw4AN3VU50dDRR50Jp6Hpj5VpbC2taWgXwx7IVpg77jlYs/o2O9gHYWNz4fDb3CODy8bN3NWS9oh04cIAmzbypU+fGHq7+/p507BTIjh07KjCyoskyvBRFURRFUYqjhuBWkITYeAKkG6GXQ8nMzEKr1eDu4Y6FsCAzM7PYxpu9vT2//LaQs2fPEh4eTr169ahWrdod83Tp2oX1O9py4MABtFotrVu3NslWFV27daXt9rYcPHiwQLlSSpYt/Z1f5/1KRnoGTh5OZKVlkZmeRY1a/ox9/23q1atXoKxnnn+WR/o8yuHDh3FwcKBly5YlHj6bnpaGtVvB67HSWpKVkXmbHP8dTk5O/Lx8MadOnSIqKoqGDRvi4+Nz1+UkJydjpy08BNzR0ob46BhThFpiCbFx1LSyK5Rur7MmJSUFZ2fnco2ntJKSkvDwsC+U7unpQGJSYvkHdCcSNZdTURRFURSzUj2gFaTZfS3Yfm4vIlODo8YRa6MtwaFXSMxKLvHKoUIIGjRoQPfu3YttfF5nY2NDp06daN++vUn3SbS1tS1U7oxvZ/Dzl0topG+NR0o1zm69iNUpZ+6z7IbNBRdefuaVIhcXcnZ2plu3brRu3fqu5m62uK815+NDC6RdTLhG41bNynRtVYUQgqCgILp3716qxidAQEAAcSKdjJyCQ0PPpIfRuVc3U4RZYh17dedY8rUCaWnZmSRrc/Dz87tNrsqnRYsWbN92rsBiTkajkU3/nKFtm9IPf1cURVEURamKVAO0giTEJnItJ5YjKaeJzoolOP0K/6YcJccoi111tCrIyMjgj8WraOl6H9Y6a46GHaKNQ0fshSMJ8Qm423lSRwQx5/u5Jqtz9FuvcVATyt6oU1xLjmZ/9Bn2yWBe+9+bJqvjv06n0/HWR+NYEr6LkzEhXE6K5K+wA9g18qFT587lGkvPXj3JqeXGmquHCUmM4kh0CD+F7WHsxAlmW1TKHDw8POjWrS9vvr6QffvOcvDged57dzGNG3fE39+/+ALKUe5iVppSvxRFURRFUYqjhuCWg5iYGJYtXkbw+WBa3teSfgP6cfHMRXo3GkhEShjnEq9hZ2/Po7UGcjjjAKmpqVVq/0QpJfv27WP172vQWugY9NRAXFxcsBcOaIQGozQijUYsNZbkGHPIzBsS62Xnw+lTB00Wh6enJ4vX/M7qlas5c+wkTYN68OHA/nf1XoaEhPD7r78RHxNH5we60bNXT5MtqlNVdO/Rndp1arNiye8kxMbz7ENj6NK1a6FGX0ZGBmtWrebw7v341wpg0NNP4unpabI4dDod38+fw5bNm9m5cStuXrWY+eTEEi1IVRkYjUZ27tzJjs2rsbaxo3u3p9m96wwGQw5PPfkWLVq0qOgQiyTVYkKKoiiKopjRvfXNugJcuHCBV54bSQ1DDVyt3Fi3bx1LFy6l1X2tuHIpjPoeDanv0RCAHGMOBn0O9vaF54tVZp99/DnbV+6lukVtDNLAG+v+x8AhvUmVKRilEY3QoNFqyDZmYZRGHGxzt9CISo2gfuv6Jo3FwcGBZ59/tlR5t2zewuR3P6OldW1cLWz5fd88Vi5ZzsyfZt9zjdAaNWrw5rtv3/Z4SkoKLz3+LP4p1tRx8Cby+CEG/7aKyfO/p0GDBiaLQ6vV0rNXL3r26mWyMsuDlJJPPngbEb+DPvfbkJ5p5Jelf9N9wJs89vgzFR3enanVhBRFURRFMSM1ZsrMPpvwGS10LajnWh8POw8auzXFIyG3l+iKRTBhybmb0qdlp7E7didDRg+pUsMLg4OD2bJ6Jy2cO+Jp74uPQ3VaOXdl2U9/8NDABzkYt5vMnAya+7Vmb8oOUmQyri4uRKdFcYGTvDx6eEVfApDbWzX5o0kM8OxEfbea+Dl60d27FfrziWzZvLmiw6t0flmwiLpp9nTxDcLPwZ2WXrXp79qMLydMrOjQKoUTJ06QHrGD8c950biWI20bOTNlpCcrfplGWlpaRYd3BwIpS/9SFEVRFEUpToW0dIQQzkKI5UKIs0KIM0KI+4QQrkKIf4QQF/L+dLnp/HFCiItCiHNCiLvbV6IcHTp0iP+9NpYxQ0fx59q1GAwGQi+F4m7nUeA8FytX1v25jnGfvotlSw3b0zdx3v4Ur38+hieffuKOdRiNRvbv38/GjRuJjo7OT4+Li2P61O8Y8txwJk+aQlRUlFmu8VZ79+zDWe9FSkoKoVdCuRp6lfS0dJyNXjRr0ZQhE57njPVhohyu0uyBIIzN0tidvRnZKJ0fl86hRo0axVdSjNDQUL74+DNGPD+cH2fNJTk5uVRlOBmtC2z5AVDfzp/tG7feNl92dja//7aMkS+OYMLY8Zw6darI84xGI/v27WPjxo3ExcXddXy3c+rUKSaMfY9Rg19m+bLfS7SFjyns+mcbTdxqFkjzsnMh5mp4ucxhllLy77//8u6YV3l7xEg2/fNPpZo7vXf3Vno20xZIs7TQ0LquhpMnT1ZQVMp/nRBivhAiWghx8qa0j4QQYUKIo3mvh286ViXurYqiKMp/S0WNK5wGrJdSPiaEsARsgfeAzVLKSUKId4F3gf8JIRoCTwKNAF9gkxCirpTSUEGxF2nBvAWs+mEpLe0b4Ka14/eJC9mwdj0anYYcYw46jQ6D0cC6c+tISk7B0cqZKf+bhlddd1ZvWoWNjU2xdYSHhzP8+RHIeEt0BiuStF/x2Av9GTCoP88NGoxrajU8bHw4cPwcf658hvlL5hAYGGjW63Z1cyEiLgxhsMZSWGMEwlMjibAKw9XVlV69ejHgsQFmq//o0aO8M+xNmlnVo5aNB0cW7GLN76tZtOKXu9qmw9HRkdScwtu1JOlT8fAqeoVhvV7P8GeHoLuip4FDTVKCU3hny2u8PGEMffrd2I81NDSU0YNfwTnNGhus+MbwJYOGPsmQl4fd9fXebM2q1cyZ+B3tHBpQx9KFf75Zzp8r1jD3l/lYWFgUX0AZuLq7khSZhu1NDXaDNGLUiBLt2VpWM775hpMr/+IB7+roNFr+/PQr/t28hQ8nfWH2ukvCxdWTqHOFx7JGJUpcXFyKyFFJqG1YqroFwAxg0S3pU6WUk29OqCr3VkVRFOW/p9x7QIUQjkAnYB6AlDJbSpkI9AUW5p22EOiX93NfYKmUMktKGQJcBNqUZ8zFSUlJYcnsRfT27kR1R2887dzo6t2amKPXaHFfC47FH0VKyeHww2jSrAiyaUWbgA60cepE5lmYNmV6iep5a/RYvJLqEOTYlvouzWjj0JOV8//ivXfG451eh5rO9bG3cqKGcx0CDU2Z9MlXZr5y8PPz40rqeYwY0Wp06DQ69DKLa8nBJd4apiwmffA5vVzup65rTVxsnGju2YiANE8WzltwV+W4urpSs0kdTsVdyk9L12dyOPMSA58cVGSeTf9sQoRm0cGrOW62zgQ4+9HXuwvff/Uder0+/7yxo97iftmAbp5tuM+zKY9792DN3OWcOHGiVNcMuY3fmV9NZ4BvZwKc/XC3daajd3M0VzLY9M+mUpdbUs8MH8w/MSfQG3OA3B7J7eGneGDAo2ZvgEZHR7NzxWperteUQGc3/B2deaF2EJf/3cf58+fNWndJPfDgI6z9P3v3Hd5k1QZw+HeyuvekLaWssvfeewhuUUEUBERRxD1xoCKo4OJzoGyQDSp7b5BN2XuU0ZYOWrp3cr4/WgulZbRNmlLPfV3vRXPyvud5koYmJ2ftE0TG3tjOJuR0InFZFahevboVM7s7iSj2oViXlHIbEHePp5f591ZFURSlfLLGENwqQAwwXQhxUAgxRQjhAPhIKa8C5P7773Ka/sCVm64Pyy0zm8zMTBISEpDy9qtvSClJSEgodIjjyZMn8dN5otXkH3IXpPMhwN+P+g/VY931NeyL2Iu3wR93H3ecHHMW4qnuWpMNq+4+xzA2Npboy3F4OvjmlQmhoaK+Bv9s2YWfUyBSmkjPTsMkTXg6+HL25Pk71Hhn2dnZxMfH33VY45HDR6jn25TTxv0cTNtGSNoWLnKCuv5N7trAMplMxMfHYzQW7wt3o9FI3NVruNrmX+U22L0yu7bsLHJ9Y3/4hpRaNsy7upFl0f+wNHkXH333+W23yvhn03aq2PrlKzNo9bjjyKVLl4CcXuusmFR8HW8Mw9YIDfXtqrFs8dIi5/ivS5cu4Y4jBm3+ns5qdv78s2l7seu9Vy1btuSpt19kasR2Fl7dy+SwLbh3qsWrb75u8diHDx+mnr0zmpsaukII6ts6sX/fPrPHy8jIKPKwbmdnZz76cjIfzoJ3fo9l+IQYZu30Z+y3k0ulh7i4crZhUXNAy6FXhRBHcofo/tsFb/H3VkVRFEUpjDWG4OqAxsAIKeUeIcQEcobb3k5hn2oKbSkKIV4EXgTuaX+9zMxMJnz7BSG71uBsC2nSmeHvjqVFi5b5zvtn+zZ+HvMltpnpJGYbadOzF6+9937eyqienp4kmVIL1J9gSqVpRX/6PPUkb7z7Bo90e5Tqhurob2o0CLhjw/dfRqOx0EetQWCwseFo5H7ORZ9Ej4EsmUEVrxrYVbQpeMFdmEwmfp7wC3/NXYJOGhC2kjc/eJ1eDz5Q6PnePt7YOBh4yK8fyZmJaIQWB4Mjh5N24enpeds48+cuYMpPU9Ab9WRpMxnw4gCee/65In0412g0oBNkm4zobmr8X09LwLt60bcDcXR0ZMKkn7l+/TpJSUkEBATccUEo34AKhO48REUq5CtPNKbg7u4O5PzeCntMQgiys7MKlN8rd3d3koxpBcrjM5OoVLF0etie7PsUjz7xGFevXsXd3b3UVm/28PDgmrHgcxdnyqKej4/Z4qSmpjJ+9EhOh2zD3kZgtPXinVHfUadOnXu6vn79+sxasJ6IiAhsbGzu+P+hLFENyXJnIjCanHeQ0cB3wGAs+N6qKIqiKHdijR7QMCBMSrkn9/ZichqkUUKICgC5/0bfdP7NG/8FABGFVSylnCSlbCqlbOrl5VXYKfl8/80ofFJWMGuEA7++5Mj4fhn8POaVvN4ryNlG5ZePPmBkFR8+bxjMd01qYty5if+N+ybvnCpVquAQ6MaZ6xfzymJT4wnVRNKzV07DzcHBgcf6Psa5hJP5criQcI62ndvcNVdvb29cfB2JT7uxgI2UkitZZ+nQrQ1nIk7Q2KYzjWw70ti2K5ciL1CzQdG3OJn8+xTWzthKS4ceNHfqQgPRjvEf/UBISEih53fo0IF053iup13DycYFB4MjUcnhGLyhYcOGhV6zds1aZo6bSSeHLnR07Uwnh64s/N9ilv5dtB5BIQSP9n2cXdEhmGROT21Gdia7Ew/z/LDBRarrZm5ubgQGBt51NeI+fZ/kWHYoiRnJQM7v40jsGYKb1M5rgAYEBCCdtcSmxeddJ6XkWFooDz3xSGHV3hN3d3eqNa7JoZjTeV9gJGQkcyTrIk883afY9RaVXq8nMDCwVLcOatiwIdccbTkVe2MRrksJ1zlBFu3btzdbnM8+GEHD7M3M6G/PxCftGdUmji/ffr5Ii0gJIfD3979vGp9K+SOljJJSGqWUJmAyN4bZWuy9VVEURVHupNQboFLKSOCKEKJGblEX4ASwDBiYWzYQ+Lc1sgzoK4SwEUJUBqoDe0uaR2pqKkf3ruOZji5oNDlfBHu66BncUfLXwpl55y2YPo2+AV642Ob0JmqE4MnqQfyzZlW+4bgTJv1Eci0tCyLX82fUJvbZn+d/M37N98H8hZeGYFtTy774HZyOOU5I/C7S/eN554O37ynnb3/6hvP6g5yIP8CZa8fYn7iJDo+15NrV67Sq2JkMUkkxJZEuU2jq34HTR04X+XlZ+Mdi6ro2RSNyXhoGnQ3Bto34/afJhZ5vMBiYMnsSSQHh7EnawJ6kdYiayUye+dttezOn/TqNpq7N0WlyepB1Gh3NXJsx47cZRc532IiXafhESxZHr2dFzFZWJm/n1c/fpHHjxkWuq6h8fX0Z++s4NsvD/B29mflR67Bp4cmYb28shCOE4OufxrMhbT87og5yIPI4f0Zuos0TnWjUqFGJ4o/57mv0LbyZc3U9iyO3sNF4hDG/jsPX1/fuF9/HNBoNP0ydzA4XPWNPHeCbUyEs16Txw/SpGAwGs8SIjo4m4dIhetV3znsd+7vb8ETtDFYsXWyWGGWSFEhT8Q+l7Pn3i91cjwH/rpBrkfdWRVEURbkba62COwKYk7sC7gVgEDmN4YVCiCHAZeBJACnlcSHEQnIaqdnAcHOs0peYmIi3S8EVOyt6GVh/6HLe7eiIcCo4OuQ7RyMELnotaWlpeR94XV1d+d+kn0lJSSErK6vQFVhtbGyYMXcaBw8e5MyZM1SuXJlmzZoV6GnLzMxkzqw5LFm0DI1WS59+j9P3maepUqUKqzYtZ9u2bcTGxtKsWTOqVKlCt7Y9aejVCLzBmG1Eq9UihCAipfAtQW5HSkl2ejZam/wvCxcbV05H3H5xl8DAQOb9NYfExESEEDg5Od0xTnxcPA52+Z9TW70dKUkFhzHfjUaj4c3332b4myNISkrCzc2tVPdRrVa9Ou06t2PL2o14u3nTuWdX7O3t850THBzM3xtyfm/x16/zbqtWdx3GlpCQwNTfJrNjwxZc3FzpP/R5unbrmu8ce3t7vvr+G9LS0khLS8PNza1Mzy80J29vb36eMZ2kpCSklDg7O9/9oiK4du0afoVUWdFVy/aIKwXvKEfUYkL3LyHEPKAj4CmECANGAR2FEA3JGV57EXgJLPfeqiiKoih3Y5UGqJTyENC0kLu63Ob8McAYc+bg7e1NZJItyWlGHO1uzB/cdjydhs075d1u0rY9e5cv5CHHGw2GhPQMUnU2hX7odXBwKFB2MyEEjRs3vm0PnZSS4S+OIPpgElWdmyKliXnjl3Bg7wG+/+k7DAYDXbvmb4g0ataQiC1X8HMOzJuXGpsaRdVaRduCRQiBl78XSbEJONm45JVfSQyldc9Wd73+XhsB9RrXI2xPGBVdboz+ikyOpHrtakXK92YGgwEPD49iX18caWlpDH76OWqlujLAqxVJmalM//g7roReYvBLL+Q719bWlu7du99TvampqQx+6llqpbrRz6s5iSmpTB35HWGXLvP8CwWHFtvZ2d3TNj7l0d2+7CiuKlWqcDJGQ2a2CYPuxhca2y4YafRsB4vELCvUHND7l5SyXyHFU+9wvtnfWxVFURTlbqwxB7RM0Gg0vDDiE96ensSBM0lEXMtgzqbrbL8UwEMP39i38om+fdlp0rP8/GWiklM5FBnDV8fO8+pHH1ukt+no0aNcPnyVWm6NMGgN2OhsqevejMP/nODChQuFXvPGu68RYXuai/FnSMlM4nLCec5ziA8+fbfI8T/5ciRHM3ZxJSGUlMwkzl0/SZxLGEOHvXD3i+/R6+++zmntSc7EnSEpI4nz189xxHiIdz56x2wxSsPK5csJTLajqU8wOo0WN1snnghszeLpc0hLK7hA0L1asWw5gSkONPGpgU6jxd0up96FU2eTnl5wr1LF/GxtbXly8Ju8tySJI5dTCIvLYMr2eEJFLTp16mzt9BRFURRFUe5b/9kGKECXrj14e8w81kV15IdtldHWeoNfpi7K15vk6OjI7/MW4PDQk/yRqeNIUG0+mzKDtu0t0wty4sQJHLIKblTvnO1x2z0O/fz8WLBsDi361yIlKJwGTwQxd+ksqlUreo9igwYNmPnXVKr18iEhMIwOQ5qwYOncvEV1zKFixYrMWTKbOk/WJMI/jKqPVmHO0tlUqVK0HltrO7z3IJXt86+2qxUavHTOhIeHF7veI/sOUtk+/2quV5NiiY+K5btvvuXKFcsMAb18+TK//zyRn76fwOnTRZ8/XN488dQzvPDZTJYntubnk9Xx7vYxP/7+B1qt9u4X389kCQ5FURRFUZS7sNYc0DKjVq1afPz5d3c8x9HRkedfGMrzLwy1eD6VKlUiQ59SoDxNn0xAQMBtr/Py8uLt994yWw6jvvzULHXdjre3N2+9b558raVyjWqc3bOZQJcbjUUpJbFZSXh7F30bmLx6g6tybu82Kjrn1LHm3B7ORV6hlo0vSWvPMmzFQIZ99DoPPVr8VXRvtWTxX0wb9zNN7SqiFRpGzl9Ot+ceY9iI4WaLcT9q1KgRjRr9bO00So0ETPI//b2koiiKoigWpj5plDEtWrTAUEFyKeE8UkqkNBGacAaPKk73vP+gUjoe6/M4R4niSmLOdiBZpmw2RRyhZfcOJVoU57Enn+CI6SpXEqOJSonj9NWLdHdrRH2/YBr51qCvXwd+/XoCKSkFv6gojqSkJCaN/x+DKrajsU81GnhXYWCldqyd/ReXL1++ewVKOSKQsviHoiiKoijK3agGaBmj0WiYPncqFTu5sjt1DbtT1xLc04833nuNgwcP5tv65X5x/vx59u/fT2pq0Ve5Lcvc3Nz4+Y/JnPJL5/crG5kZ/Q+1+3bkw88+LlG97u7u/Dx7Mif9UvntzGoq2njh4OGCb4Wc3RQMWj2VtB633Zu1qA4cOEA1nSd67Y0BERqhobbeh+1bt5klxr+klJw5c4YDBw6U2nzWqKgo9u3bR0xMTKnEu69JVAO0jBBC1LV2DoqiKIpiCf/5IbhlkaurK+O+/xqAixcv8uoLr3Fg/RG06EjWXmfUNx/TsWNH6yZ5D2JjY3n1hVdJDU/GTtgRY7rGsHde5smnn7R2amYTFBTEL9Mnmb3eypUrM3HGZBYvXMyOH//Gyzv/5u/pMuuuKy7fK3t7ezJEwd0XMmQ2jk6OhVxRPFFRUbw37GUcEhJx1uoYnZbC0PfepffDD5stxs2ys7P58qP3uHzwH2q66Tgel0XtNt1599MvSnWrHkUppt9ytyqbAcyVUsZbNx1FURRFMQ/1KawMM5lMDH/hNSom16ahUxvqObWgsaEjn771OdeuXbN2enf1zoh3qBDtTUePDrRwb05P9+5M+WYyp06dsnZq943uPbtzynSV5MwbvcfRKXFct82kYcOGZonRpEkTIvVpRKfE55UlZqRywhRD5y6F7oxULCNHjOAxvS0jatRmYLVgRtWqx6xvxhEaGmq2GDebPmkinpd3MqFrBV5u4sVPXSvAsXUsmj/XIvHKDQsuQiSEmCaEiBZCHLvN/TWFELuEEBlCiHfu5VohxJNCiONCCJMQorDtve5LUsq2QH+gIrBfCDFXCNHNymkpiqIoSompBmgZduLECWS8Djc7z7wyG50tPsYgVq1YZcXM7i42NparZ69S0fnGXp86jY6aNjVYNHeRFTO7vzg7O/PFhK/4M2EPq6L2sixqN5vlaX6Y8rPZevG0Wi3fT/mFFZmnWRyxl78j9jMv/gCjfxpvtn02w8PDkdEx1PC48Vq21el4wNObFX/+aZYYt9q07E/61rvRcyyE4LkGXqxaONsi8cqDnEWIRLGPezAD6HmH++OA14Bvi3DtMeBxwLzjxcsAKeVZ4GPgfaAD8D8hxCkhxON3vlJRFEVRyq5yOwT3wvnTzJz+O88NHGrV4XZSShYvXMzkX6aRnpKBu7cb73/yDq1at7rrtampqWhlwV+RFh0pKbffZ/Lw4cOM+fQrIsOiMdjq6T+4H88PHmiRfUtvJz09Hb2mYO42WhtSksyzeM5/RfMWLVi+ZQ0nTpzAYDAQHBxs9t9l1apV+XPdck6fPk12dja1atUy63YjaWlp2GsLvh7sdXqikpPNFudmRmMW+lv+79vptGRmqNffnVhyLqeUcpsQIugO90cD0UKI3vd6rZTyJFCqf99KgxCiPjAI6A2sBx6SUoYIIfyAXcBf1sxPURRFUYqr3PaABvkZiDozhymTfrJqHosXLmbSmJnUk21p7fQAFRPq8v7LH3PsWKEj0PJp0KABSdpYMo03Fh6S0kS0uEKXbp0KvebixYuMGPQWntFVaeX0AA20HVg4YRm//2r+eYp34ufnB46QlJGUr/xc2nl6PNyjVHMpD7RaLfXq1aNGjRoW+6AthKBmzZrUrVvX7HtdVqlShUhMxN+08JCUkq3XounUq5dZY/2rXrPW7Lx4PV/ZhnPXaNmpu0XiKQB4CiH233S8aO2EzEEIUUEI0UcI8X0phv0ZCAEaSCmHSylDAKSUEeT0iiqKoijKfancNkCFEIx4NoiNaxeSlZVltTwm/zKNei6t0GsNADgYHAk2NGLihN/veq2NjQ0fjf6AvckbOHf9BKFxZ9ibsJne/boTHBxc6DXTJs0gUNbEycYVAJ1GTx3X5syftQijseBCM5YihODL78ewLWUHR2KOci7uPFtitlGlTVU6dOhQankoZYNGo+G9sWMZf/YE6y+Gsic8jAknj+LbphXNmjWzSMxX3v6A2eG2TD8Yxa7QWH7fH8WK624MGvaqReKVFxJR7AO4JqVsetNRut98mYEQQiOEaCCEeEUI8YcQ4gKwF+gDXCzFVHqRs/hQ2k152QNIKf8oxTwURVEUxazK7RBcAK1Wg4eLluTkZNzc3KySQ0ZqJgZHQ97trOxsMhOMbFy2mi98RzPoxeepWLHiba/v1qMb9RvWZ8WyFaQkp9Kj19vUqFHjtudfOHsBN7vKebezTdmciTlGaHQoL7/wKrXqBXP0wHF0Oh1PPvsEXbt2LXGPmslkYsXyFSxbtAydTkefZ/vQpUsXGjRowOK1f7Jy+UquRV9jSKehNGrUqNwNlbtfGY1Gli9dxvq/l2KwteXR/n1p36GDxX4/zVu0YMqyZaxatozE+HiGd+5MvXr1LBbPw8OD6X+uYMO6dZw9e5L6tevzeufO6PV6i8QrH9R2KuTMQ40FVuQeH0opw6yQxwagK/DvGHV7YB3Q2gq5KIqiKIrZlOsGaGpaNnFJGlxcXKyWg5uXKykJyTgYHMnMzCT0wkUSMuLwdajEqeVX6b9qIFPm/XbbHk0AHx8fhgwdck/xmrZsws4/jlDZUBOTNLL+1BIcslypo2/FvtWH2PnnARr4NcfW1Zlv3/mFvY/v46NRI0v0GN974z0u/nORWo41MUojP737P/Y/sZ8PPv4AV1dX+j/Xv0T1K+YnpeTd4SPQnrhMD+8gMpOz+WPkGI4+cZBX337TYnHd3d159vnnLVb/rQwGA70efBB4sNRi3tckyHtYzbacmwC0AtoALoCLEGIncFzKUn12bKWUeROkpZTJ//aAKoqiKMr9rNwOwc3INDLyx3MMGPyWVRch+uDTdzmcsoNrKVFERl4lMeM6l02naRTQigCXygRrmvLV5+PMFm/AoOe47hjOpfizhMaeQZ9hi68+EGcPR7LTTTRybE9afCbONq40dmvDhqVbiIiIKHa8kydPcmrnKVp5tcTVzhUPew/aebdj05JNXL161WyPSzGvQ4cOkXL8Ak8E1cPb3okAJzcGV23CpsVL74stfhTLkOQsQlTc426EEPPIWUCnhhAiTAgxRAgxTAgxLPd+XyFEGPAW8HHuOc63uza3/LHca1oBK4UQa0v0HEg5SkrZHWgGfJdb/A6wTwixpiR1F1GKEKLxvzeEEE2A268+pyiKoij3iXLbA3otQcvAVybQokULq+bRslVLfp0zgYkTfmfNn8sJcqpFd79HcbTJ6ZV1t/di37mDZovn7u7O3L//YNKvk5kzYx4V7WtTsWIAl5LP4KmpgAC0aMnIyMDOzg5XkzfHjh3LWTSoGEIOhOBt8spXJoTAW3hz9OhRKlSoUKLHExUVxZZNW9AZdHTu3NlqQ6nLm4P79lM7d57wvzRCUMPOlRMnTtC+ffsi13n+/Hl279yJm7s7nTp3xs7OzkzZ3l1qaiqbNm4kKTGBVm3aEhQUVGqxlXsnpex3l/sjgYCiXCul/Bv4u+TZFeCam4s/4AtkAJctEOd23gAWCSH+/YawAvB0KcZXFEVRFIsotw3QgIpBVm98/qtu3br8Mvkn4mLjcIuonNf4BMjITsfe0bwf1L28vPho1Eh8fH1YM3EH9vb2OGQ6ESmjADBJIzpdzq8+Q5uKl5fXnaq7I98KvqRqUwuUp4gUfHx8il0vwMJ5C5jx/W/U1vthlCamjPuZd8d8QueunUtUrwK+fn6cN6YXKI/JTsfb27tIdUkpGf/lGI6t3UhjOxfOmrKZNO5bxk3+/Y5Dy83l6NGjjH7jJTp5S1z1kjEzJ9Cw51MMf+tdi8cuj/7rc0CFENOBekA8OT2u/wDfSykTSjMPKeU+IURNoAYggFNSSuutqKcoiqIoZnLXsalCCNvc5ecnCCEWCSFmCSHeE0LUKY0Ey5OXXhvKiZT9ZOVuq2I0ZXM8aS+Dhw20SLzHn3yMWJsrJKZfx985iHiiicoIw87BFr1eT3jSJWwraGnYsGGxY7Rv354kx2SuJt0Ybnsx4RJ6Xz3169cvdr3R0dHM/P43BgR0pFWF2rT1q0t/33aM/3g0qakFG7xK0XTu2oWTIpWLCTnDbaWUhMRcweTrdsdFrgqzZ88ezq/dzJvBjegYWJVHg2owzLc6o958G0tPmTOZTIx9/3XGtHKhf0NfetepwPhO/pxat5AjR45YNHZ5VcJVcMuDFoAfkJB7JGK9oa/NgPpAI6CfEGKAlfJQFEVRFLO5YwNUCPEZOd/+tgL2AL8DC4Fs4GshxPrczbLLPCklYWFhJZrvWFIdO3bkjc+HcVRuY3/KRkKyNjLgzad4vM/jFonn7u7OxJk/E+11nv0pG3ENcCLG5zyXXY7xT+Jq7BuYmDLr9xKtQqrX65k8ZzJxleJYfW0Nq66tRtaV/DbjtxLVu3nTZmrp/dBrbnTS2+lsqKz1ZN++fcWut7xLSEjgwoULZGZm3vE8W1tbfpwxhU0O6Yw7u5Ovz+0ktIobP0wu+u9t3d9L6eTpl+86X0cnbJPTLD4PODQ0lABdOj5OtnllGo3g4Sp2bFy5xKKxS1t2djYXLlwgLi7OonEsOQf0fiClrE1Oo28TOY3Rb4FIIcROIcS3pZWHEOKP3NhtyWmINgOallZ8RVEURbGUuw3B3Sel/Ow2930vhPAGAs2bkvmdP3+eUW+8gV1KCgDpTk6MnjDBKvPEHn38UR5+9GHS0tKws7Oz+AJJNWvWZPHyBaSmpqLX69Hr9aSnp6PRaDAYDHev4B74+fkxbe50s9ar0+swSlOBciMyb/iwckNmZiZjPv6Uozt242Vw4GpWCgNGvEyfvrefMhYYGMikuX+QlpaGVqst9u9Nq9eRbSq4x6xRWv53pdVqySr4MiHbKNHpzfP6Lgs2bFzH5Mn/o2pVT6KjE/H0DOKTj0ebPY4ETP/xVXCFEA2ARUAMUAc4BvQk5+lpU4qpNAVql/LKu4qiKIpicXf8dCilXHmX+6OBaLNmZGaZmZm8N3QorwYE4B+Y01a+nJDAO0OHMn/1aqs0ZjQaDQ4ODqUa097+xur9tra2dziz+MxZb+fOnZky7meaZFfHTmcDQEJGCleIp1mzZmaLU1788PU4NPvP8nbVVgghyDIamfTjb1SqWuWuz1dJFwt66Kkn+Xbza9Ty8EGb+4XK+euxaL09ijyftKgqVarEdb0rF2JTqOKR838qy2hi8fk03n/bMiMLStvZs2eZO/dXJk8dgoNDzv+x1asP8NXXn1k3sfLrf8AAKeVuIUQI8Ajwp5SyI1Cawy+OkbP4kVpOXFEURSlX7qn1JYRoCnwEVMq9RgBSSlnmh9/u2LGD+no9/k5OeWWBLi7UiI5mz549tGlTml9oK/fKzc2Nd8d+wviPRhOk9cSE5DLXGfvTeLP13JYXJpOJ7avX80G11nnDYPVaLQ/6BDN/6gyLN9jr169Pl+efZez0WdS1cyZRGomw0fDD1MkWjQs5Ky5/8eNEPnxlCDVso3DTw67obB4b8nqpLIBUGpYsXcSgIe3yGp8ADzzQhEULfjN/MClyjv82Fynl7tyfhZQyVghRut8Y5vAETggh9pKzAi8AUsqHrZCLoiiKopjNvXb/zQHeBY4ChQx4K7vi4+NxLWROm5tGQ0JC6SxquHnzZn75biJxsfEEVQnknY/eonbt2qUS+37WuUtnWrZqyf79+9HpdDRt2rTYjc+oqCj+9813HNl/EBtbG554rh9P9+9nlT1i161Zy6xfJ5F0PZ5KwdUY8cE7VK9evdj1ZWdnYxACzS2vczdbe65fK53Ok4FDX+ChJx7n0KFDuLq60rBhw1J7bitVqsTs5es4cOAASUlJDGzSBBcXl7tfWAbt2r2TWbMmkZAQh4eHD0OGDCchIQ4vr5oFznVzs0ybyKQaoFohhE5KmQ1ohBBPAdbYHPczK8RUFEVRFIu71wZojJRymUUzsZDmzZvz1w8/0EPKvA/oJinZn5zMgKaWX89h7eq1fPX+99RzbEk1G0fizsYw7NlXmbZwEtWqVbN4/Pudvb19sfakvFlycjIv9h1AO21Fhvm2IT07k/W/LuBqeARvfVC6W3UsWfwXi7/9lT7+DXGpaM/FyBjeGjCUXxbMIjCweNOpDQYDjj5eRKYk4Otwo+G1N+Yy7fo9YK7U78rd3Z3Ona2zRY5Go7nvh2b/s3MHs2Z9z8efPo6fnwehoZGM/vxTmjfrzvp1R6he3T/v3Li4JK7FWGZh1nK0mm1x/QgEAyeACKAH8HxpJyGl3CqEqARUl1JuEELYA9rSzkNRFEVRzO1euyhGCSGmCCH6CSEe//ewaGZmEhAQQKvHHuO7Y8c4EhXFkagoxh87Rpd+/Sw+Pw1gwvifaeTcBgeDIwDu9l5UFfX5dYIFhs8phVr29xJqZbtR26MiQgjs9DY8VLExm5asIjk5udTykFIy4+ff6B/YDBebnDm5QS5edHeszKxJU0tU9wdjPmdm5HF2Xj1PaPw1Vlw+zhlneLr/M+ZIXSkFM2b8xqjP++Dn5wFA5cq+fDDyIc5fOMnpU2n8b8JKjh27yPr1B3nz9T94dcT7Vs64fJJSTpVSnsj9+QEp5RApczdRLkVCiKHAYnJWnwfwB5aUdh6KoiiKYm732gM6CKgJ6LkxBFcCf1kiKXMb/tZbhHTsyNolSxAaDa8//niJ9qgsTGZmJv/7/idWL12L0WikUfMGfPjpB6QkpmLjmH+RFy9HX06c3ElGRgYrlq/kny078Q/0o9+zffHz80NKyb59+/h7wRK0eh1PPP0YjRo1umP8f3b8w/df/UjctXicXR0Z8fZwunbvatbHeDvx8fGMHzOOfdv3gBB07NmJN959M9/CR5a05M+/mDlxGhkp6bh4u1O7UV2SrsVTs35tHn+qD6ePniDQ3j3fNUIIfA3ORERElGiu4OXLl/n2i7GEnjyDRq/lwaeeYMiwFwsdfpqVlYXIzMZWp89XXsXVm4XHTxY7B4DatWsz+e+FLFm4iDMXL9O89QOM6t0LGxubEtV7J4cPH+anMV8RdzUSvb0d/V4ayqOP3xffS5XYxvVrmf3792SkXMfexYvBIz6kdZu2JaozNTURHx+3fGXBwQFERa1k5ozFbNy0gRXLduLm5sk3X/+Gv7//bWoqmf96D6gQYhoUfBKklIOEEJ9LKUeVUirDgebkbIGGlPJs7srziqIoinJfu9cGaAMpZT2LZmJhjRs3pnHjxhar/53X3yNidxwtXLoghIaI3Zd4vu9gHJztSM9Mw1Z3oxEanXyV6i2r07/Pc2Re0eJtCCB8xymWLXiO7yeNY/3qDWxYtJ0AfXWkNPH26pH0eeFhXhnxcqGxDxw4wEfDP6OBY2tq2DuRlpLKmHfHo9Fq6NzFskMiTSYTQ58dQkCcF4959EAiObb6NCPODGfqnOkWjQ2waP5CFn83g4d9m5FpyGLyzr+QB6JoUrUepw5t49nZC+n15CNc3n2eIFefG3lLE5GZifj5+RU7dkJCAiOeG8KD9lV5pFI7Mo3ZrJu3lnEx1/hg1McFztfr9UgbHWnZmdjpbsxlvZAQTfXmtYqdx7+8vb158dXhJa7nXpw9e5YvXn6VFwJrUKFGY5IzM/jj+58xZmbxxB22fikPtmzayN8/vctXXZ1wd7QnKuE6n48Zjs3oaTRp0qTY9To4uBAZGYev740vS06fDsPPLxCdTkeP7j3p0b2nOR7CbUlAbfrBijvct7XUsoAMKWXmvwuLCSF05PyKFEVRFOW+dq9DcHcLIdSqObcRHh7Oib2nqelWH41GixACf+cgbK4707JjCw4l7iA5IxGA2NRoLsijBAT5k3VJTy3XxnjYexPkWp16Nm344M2RrP1zE41d2+Pj6IevUwBNXTuyYPqfxMTEFBr/f+N/prZdMxwMOSv92untaeDYip+/m2jxx75r1y70MYIa7lURQqARGup71uL6uVhOnTpl0dhSSmZOnErvCi2w19uy8fweOrnUpY1rLfSpJpp516SNthrnTpzmlD6RYzGXMEkTqVnpLL1ygG5PPIijo2Ox4/+96E8aCw+quOU0bA1aHb0D6rFrzUYSExMLnC+EYMjrw5lzaT/X03P2pA2Nj2ZdcijPDxta7DysYdZvv9PHO5AKjs4AOBpseL5aHeZOsvzKt9Y2+/fv+LCLI+6OOT3ZPi4G3utoxx8Tvy1RvYMGvcxnny7mypWc/+fnz1/lqzFLGfT8sBLnXBRSimIf5YGU8i9gFXABOA+szC1DSrmpFFPZKoQYCdgJIbqRszfp8lKMryiKoigWca8N0LbAISHEaSHEESHEUSHEEUsmVlKpqamYTJZfsDc1NZUlS5aQnpxBtjEr331OwhVXRxdG//wJ13zOsz9zA5raSUyZ/xtHQ44T4FAl3/kOBieuhkXikumdt50GgBAaXLO9CQkJKTSHq2FXcbXLP8TU3uBIQpzlV/kNvRCKq8mpQLkbzly+fNmisU0mE6b0bGxyexOvxEdS1b4CIEhOSSE9I4Oqrv6cPnqSyfNnkdrcj0lRO1mYepzurz3Ha++8VaL4oafPEuCQf8ikEAJvgyPR0YVvj/vQo48wZOyHLOUKP4bt5HigjgmzpxEQEFCiXErbxbPnCHLN/5qz0+khIxOj0WilrEpHWlIcXk75V2MO8rQl+uqVEtXbqmVrhg79kG/HbeW5/r8x8Zd9vP/e16W8YrbAJIt/3M+EEG8IIRyEEL2B0+QsRvQjcFYI0csKKX0AxJCz+vxL5DSKCw6tUBRFUZT7zL0OwbXsuC8LiAsLY8Cjj/L9lCkWW2xo86bNjH5/NG5ZnmRFp7Mkag6tqnbC36USAPFco06DOrRr34527dvlu9bT24PYs8k42jjnlUkpEVpBtjazQKxsXSaurq6F5lGtZjWuHYnC0+HGENOE9Ov4+Ft+ulDtOrVZKhYXKI8hzuL7MGq1Wmyc7UjJTMPBYIdeqyciJQYDOgxaHRGhV8jSSwzeNnh5efHZ11+aNX7dpg05uHcxQS43nmeTlERmJt9xaG/nLl3o3KWLWXMpbbUbNuDUwVM08rkxDzEpIx2dkyNabfleqNPF058rceFUdL+xL+fJiBQCqzYocd3NmzWnebPmJa5HKZaBUsofhRDjgXZSyssAQohAYC05DcBSI6U0AZNzD0VRFEUpN+7YAyqEcASQUl4q7Lj5nLLGy9aWJ+1s+PJ9y6wUmZiYyBfvj6aDU2eaeTenpX8rmto1559zG0nPSuNM3DFsKwo6duxY6PUDX3iOC1nHyMrtNZVScj7hOA8//hDpTvEkpMflnRubGo3JJf2220y88d5rnM4OITo5AiklsanRHE3bzdsj3zT7475Vo0aNcK7uxv7ow2Qas0jPTmdn1H5qtKxNUFCQxeMPf/cNlkTuJDrlOnYGG/65fgInrR0uto646Oz5J+oorj4eFond+6GHOGuXzt6r5zGaTFxPT2Fu6F4eee7pUluAyVoGDnuJZQlRHIuJREpJRFICv507xksl7FW+H7zwxki+2JDB6chUpJQcvZLMN9uNDH71PWunVnISkKL4x/3t327ta/82PgFyfy58/oMFCSFChRAXbj1KOw9FURRFMbe7DcFdKoT4TgjRXgiRt+u5EKKKEGKIEGItZbh3tI63F9Hnz5GSklKs600mE/Hx8YUOKdy6dSu+2X7Y6HJ6QXwr+OLvXwFPGw+2J6+i7cDGzJg3/ba9QfXq1eOd0a9zKHszh1N2sCd5HdU6+/HpFx/z+6xfiXQ7zYHkzexP2kSC7xUmzZpY6MqqAMHBwUxZ8Bv2TUyEmDZD7WQmzvlfiRZEuVdCCH6d9hvNB7VjU/ZutosQuo94iK+//8bisQG6du/Kx7+O5Zh3LOeTIvB0dmPu9R0sj93HzMhN+Hv7cu1KpEVi29vbM2neLAzd6vH7tQOsEGE8/enrDHn5JYvEK0v8/Pz435w/OBtckTGXjrPK1sjrE76lc9fSWXnZmho3bsJ7383mj4g6DPpb8ldCUz7/eYHFe/xLgwRMiGIf97kQIcQvwF4hxGwhxEO5x1zgoBXyaQo0yz3aAf8DZlshD0VRFEUxKyHvsuRh7tyX/kAbwA3IJmd+zEpgqpTSMp/uS6iet7f8++k+fHr4KL+vXIWTU8F5infy16I/mTzhd3TZWjJEFn2f78ugF4fkzc1cunQp80ctooF3w3zXHbx2gKHjhtD1Hj+IZ2dnExERgaurK87Ozvnuu3r1KlqttlT2Ky0PHun0AP1cWyKlJCEjBWcbB3QaLbOubWfFtnXWTk9RzE4IcUBK2dRc9dX0qCCn9h5U7Ovb/vGVWfMpTUIIW3LmWjYFXG69W0r5UOlndUsSQuyQUpZsv58Satq0qdy/f781U1AURVHKqHv9XHLXOaBSylWU8twXczkbF4eTf0CRG59bt2xhxteT6e3TAYNWj9FkZM3k5Tg6O/NUv6cAaNeuHT9qJ1DbWAe9Nmc1zExjJjHaKFq1anXPsXQ6HYGBgYXeV6FChSLl/V/XqVc3DiwNoaVPLbzsXQHYH32G9t07WTcxRVHKPCllOjDB2nn8Swhx875hGnIaxkV7M1MURVGUMuheFyG678RmpDMtOpYfphd9L8qpP0+hrXtTDLkNS61GS1uvpsyZMiuvAeru7s5bn77J95//QAWTHwi4qo3go7EjcXBwuFP1ioW8OHwYw/e/xLIre6kgnYgUyWT72vLxW69ZOzVFuW+Ul+1UiksIMepO90spPy+lVL676eds4CLwVCnFVhRFURSLKbcNUCffCsxdtQq9Xl/ka2OvxeLsWDdfma3OhvT49HxlDz3yEG3atWHLli1oNBo6dux425VqFcuzt7dn2vxZhISEcPbsWapUqULTpk1vO3e2MBvXb2Du5OkkxMfTunNHBg8bWm5/p+np6cyeMYMNS1disDHwSP++PPbEE0V6vpTy57/eAAWSbvrZBvh3W5bjpZmElFIN3VAURVHKpTs2QIUQq4BXpJQXSycd83FycipW4xOgcfPGhP5zmaruQXllUckxVKoaVOBcd3d3Hn/88WJmqZibEIImTZoUawGmWVOns2HyPB6sUBcX98ocWHuIoVueY+ZfC8rdqrYmk4kRg1+gUlQKr1SoTqbRyKqfpnPy8BE+/nK0tdNTrCRnEaL/Ninl9zffzt2WZb2Uckhp5iGEuOOS0rfmqSiKoij3i7t1dcwA1gkhPhJCFK81dx96+Y3hHBHnOX7tDIkZSZyJu8D21BDe+bQcbLOgFCojI4OFU2fxbFALPOyc0Gm0tKhQjepptixfstTa6Znd3r17sYuIo3vFYGx1epxtbHm6cl2Obt7B1atXrZ2eYjUCKYt/lFMuQEUrxG0KvAz45x7DgNrkzANVc0EVRVGU+9Yde0CllAuFECuBT4H9Qog/uOkL8vvpG9hz586xduUqNEJDjwd7UaVKldue6+fnxx9L5jBnxmyOHjxKtXbVmDHkU/z8/EqcR2ZmJhvWb+DowWME16pOz149sbOzK3G995vMzEzWrV3LqSMnqV6nBj169sDW1tZq+YSHh+Ojc0B7y/DTao5eHNt/kKef6WelzCzj+OEjVNPn38I3KysLz9Rsxo0Zy7ARr1KjRg0rZaco1iOEOAJ5e8poAB/gMyuk4gk0llIm5eb1GbBISvmCFXJRFEVRFLO5lzmgWUAKOXNhnLgPR2jNmjqdpZP+oLl9ACbgnTl/8eSrQ+j3XP/bXuPp6cnr77xh1jwSExMZ+PQgNFF63DReHPjrMJN/nsKMBdP/U1utxMfH80K/gXgl2BBgcGf9qhCm/zKJKfNm4unpaZWcvL29iclKQUqZt9UOQHhqPEHBDa2SkyVVqlKZDdlpebfjr8cTGxXFpZgomh8O5fPnX6LjM0/y4ojhVsxSsYZy3JN5rx686edsIEpKWXAzaMsLBDJvup0JBFkhD0VRFEUxqzsOwRVC9AQOAfbkfBM7Skr5+b9HaSRYUlFRUfw5aRYvBLWlkW8VmvhW4YWgNsz9eTJxcXGlmsvEn37DMcqN+m5NqehSiTpujfBLqsy4L8eXah7WNnHCz9RMcqezb0OC3QPp4FOfxpn+TBhnvQ51R0dH2vTswsqwo2QZcz5rXkqIYV92FI891cdqeVlKh44dCbWVHI0JJzs7m+jISM4kxeHo4EjPqnV4Lbg5G+cu4vLly9ZOVSllsgRHeSClvAwkk9MLqgcChBDzhBBBQohb9we1pD+AvUKIz3JX5t0DzCrF+IqiKIpiEXebA/oR8KSU8gMpZWppJGQumZk5Xxzv3r2bOnrPfEMrdRottQ2e7Nmzp1Rz2rJuC5VdqucrC3CuxMG9B4GcVUlDQkI4c+YMUt7bx7nw8HD27t3L9evXzZ6vpezctJ16XlXzldVwDyRk594C52ZnZ3Po0CGOHTuGyWTZzvd3PxlJrae7MTFyDz+EbmW/ZwY/zpyMu7u7ReNag16v55c/ZnC2micfHdnM9xf2E2ej4ZVmHQHQCEFTe3e2b91q3UStwGQycfToUQ4fPkx2dra10yl1ElHsozwQQswBDgDLbzoezv33mdLKQ0o5BhgEXAfigUFSyrGlFV9RFEVRLOVuc0DblVYi5nb1YiivDHiObo89ToYo2HBJw1jq+3Xa2tmSlZaJVnNjzqdRGtHqdKxasZrxX3yLq/QgiwwMXjp+nvLTbeedpqen885r73L24DmccCbWdI0Hn+rNW++9mW8IaVlkY2dLRnYWdnqbvLJskxHdLasW792zh8/f+Qgf6YQJyXVDBuN/m0BwcLBF8tJoNLz46iu8+OorFqm/rPH09OTrCT+yd+9e5r3/Of0q5d96KFUacXT6b611curUKT4ZMZwgjUSnEZzNMPLhuG9p0rSptVMrFVKCqbx0ZRZfPSll5ZsLhBAhUsrGVsjFHkiUUk4XQngJISpLKUOtkIeiKIqimE253fCvgp0t7dIS2LZ2Daflda6nJ+fdF5uWRKhIomXLlqWaU7+BfTkWfzBf7+bJ+CO069qW8Z9+SxuHLjRyaU5zl3b4xVXm9WFv3Laub7/+jvgDybRz7UJD12Z0cuvB5nlbWbNmTSk8kpJ5/Nmn2RZ9JN/zsCP6GA89fWM7m6SkJEa9/gFPuragt09THvJpRm+berz94oj/ZK+UJTVp0oRLIouryQl5ZQnpaRxIj6dzly5WzKx0ZWVl8fHwl3m3sg+v1qnKsFpV+LhGRca8/QYpKSnWTq/cEEL0FEKcFkKcE0J8UMj9LkKI5UKIw0KI40KIQTfd92Zu2bHcYbG2ueUNhBC7hBBHc691LkGKqwspW1+C+oold9jt+8CHuUV6YHZp56EoiqIo5lZuG6AArfx9iThxjE+++4q58UdZFH6ABWH7WZh0nHG//4TBYCjVfPo83YdWTzRjW8I6DifuY3vCeqp1qoSjoyOVRFUM2hv5eDv6knI1jUuXLhVa14aVGwl2q5V3WyM01HFuwNxp8yz+OEqq7zN9qfJAY2ZFbGJddAh/RGzGq30wg4YOzjtn08aN1ND44GS4sf+mh70LvlkO7N+/3xppl1tarZbxkycyO/Ey0y4eYdbFo/waeYJR//sOp/9QD+jevXupa6PBx/HGyAg3O1vaONmyZcsW6yVWyiw5BFcIoQV+AR4gZ0uRfkKI2recNhw4IaVsAHQEvhNCGIQQ/sBrQFMpZV1AC/TNvWYK8IGUsh7wN/BuCZ6C40KIwNx82+Xux/lDCeorrsfIGfqbAiCljEBtv6IoiqKUA/eyCu59zUajpVatWvy9cTUnT55ECEHNmjXRaEq/7a3RaBj56YcMe/UlLl26hL+/P97e3nw1+mv0moKNYR160tLSCpTL3HFyGpH/MRi0NqSmlP2puhqNhpGffULca8O5dOkSFStWLLD6bWpKGnq0Ba41SG2hz4lSMlWrVmXh2pWcPn2a7OxsatWqhVZb8Pkvz9LS0rAvZPi6vRCkpZb9/1fmYuFVcJsD56SUFwCEEPOBR4ATN6cAOImcuQSOQBw5q9FCznuWnRAii5zhqRG55TWAbbk/rwfWAp8UM8d3gTlCCF9gKjAdWAB0KGZ9xZUppZRCCAkghCjdOSOKoiiKYiHlugc0LDEJrbsHzs7OaDQa6tSpQ+3ata3S+LyZu7s7jRo1ytt6pUfv7oQZL+YbkpqWlUaaIYXq1asXuF4IQc36NbmaFJ6v/Gz8KXo+3NOyyZvRv89DYVuvtO/YntPZkZjkjfm7WcZsLhiv0fQ/Mh+vtP375UzdunX/c41PgObNm7M3IZVM440dN4wmE9sTUmjTtq0VMytdUhb/uAf+wJWbbofllt3sZ6AWOY3Lo8DrUkqTlDIc+Ba4DFwFEqSU63KvOUZObyHAk0DFYjz0f2XlbrvSG5gtpfwK6/Q8LhRC/A64CiGGAhuAyVbIQ1EURVHMqtz2gMZnZPLDhQi+mlT2368bNWpEi17N2LlyCxVERbLIIFxcYuxPX962IfDpmE8Y1HcIMbHROGtciJUx2FUx8OzA2+9tej/x9/fnkcF9mTNtAfVsKmKUJg5nXOHlka//p4aFKqXH2dmZZ998i09//J4ens5oNRrWxcTzwPNDqFChgrXTu194CiFuHiM/SUo56abbhXWv3tp07UHO9l+dgarAeiHEdnKG3D4CVCZnVdhFQohnpZSzgcHA/4QQnwLLyL9/ZlElCSFeB4YAz+X2xJbqe2VuzAVATSCRnB7eT6WUpT4XVVEURVHMrdw2QJMRfDD+W4utmFoUoaGhzJg0g9BzoTRu3pjnBj+Hh4dH3v1CCEaN/pTjfY+zaf0mXFxdefDh3vnOuZWfnx9L1v7FmlVrCD1/kcbNn6Jdu3blqudqyLChdOrehbUr16DX6xn+UC/8/W/tLLm9hIQE5sz8gwP/7MG/UkWefeH5MvF6uN+cOXOGuVNnEHHpMo1bt6DfwAG4uJTOdoipqaksnr+Af9Zvws3Lk35DnqdBgwYWi/fI40+QlpHJ7F8nYTQaeXLwIPo/P+juF5YTEijhZkfXpJR3GqIQRv7eyQBuDKP91yDga5kzJOScECKUnIZYJSBUShkDIIT4C2hNTi/lKaB7bnkwOb2XxTUAeB0YJ6U8nDv09eUS1FdkuUNvl0gpm2CFBZAURVEUxZLEve43eb8J9nSVLWrX5IXPv6JDp05Wy+Pw4cO8NeQt6uhq42nvyZXkMK7YXGHWn7PyhuAq5hcfH8+gPs9QO8ODmm4BRKfEsyXpNCN/HEOr1q2snd59Y/fOXYx760N6uVfB18GFY3ER7NMkMnXRPFxdXS0aOyMjgyFPP0P1RGjiEUBcegqrYs7T/4M3eOjRRywSc9zoMYSu3UY3rypoNRo2x4Ti0LQ2Y77/1iLxSkoIceAuDb4iqe7uL3/s+lKxr39w0ag75iOE0AFngC5AOLAPeEZKefymcyYCUVLKz4QQPkAI0ICc3tBpQDMgDZgB7JdS/iSE8JZSRgshNLnlW6SU04r9QMoAIcQvwAwp5T5r53Kzpk2bSrUQnKIoilKYe/1cUm7ngNrpdHzcqAq/fvUlJlMJv9Mvga8+/YrWjq2o5FoJB4MDNd1rUDkziMm/lv2hwfezOTP+oE6mJ819a+Bs40A1d3+e8m3Bd5+Ppbx+6WJuUkq+/2IMQwIbUdOjAq629rT1q0ZzkzNzZ86yePwVy5YTFG+ia0AN3OwcqOrmzYtVmjL5uwkW2YonIiKC/as3MKByI/ycXPFxcKZvUAPC9x7m9OnTZo9XVllyFVwpZTbwKjmLBJ0EFkopjwshhgkhhuWeNhpoLYQ4CmwE3pdSXpNS7gEWk9MgPUrO+9e/w3v7CSHOAKfI6VGdbr5nxGo6AbuFEOeFEEdyt5g5Yu2kFEVRFKWkyu0QXAAnGwOOpkwSEhJwc3OzeLzExEQ2bdpEemo67Tq0w9/fn5iIGJp55P8ioLJLZf755x+L51MU6enpbN60ibjYOBo3bUJkZCRhl69Qr0F9GjRogChkddCybN/2XXRzy7eXPM42DmRFp5GRkYGtra2VMrt/ZGRkYExMwcXLPl95fQ9/Fu3YBa+/ZtH4B3bspJ5r/lECNjo9XlobIiIiCAwMNGu8Y8eOUcvGBaPJSFJiEhKJk5MTtfXOHAw5SI0aNcwar6yy9NczUspVwKpbyn676ecIcofTFnLtKGBUIeUTgAnmzdQ6hBCBUsrL5GxVoyiKoijlTrlugJqkJCHLiIOD5Vev3717Nx+9NpKK0h+dScu076bw9It90dvqyDJmodfq8869nnadCtX8LJ7TvTp79iyvDXoF/ywPbLJ1jL88BltbA239G7JaLMa1VgUm/P4zer3+7pWVEX6B/sQci8fRYJdXZjQZyRSmUt//9X5lMBjIwES2yYTuppWjI1MS8asRYPH4FQIDiDqxn0DnG3OhpZTEZaRZ5AslLy8vQhPjuJhyDnuNDgHERUVzIfs6zSv4mj2eotzGEqCxlPKSEOJPKeUT1k5IURRFUcyp3A7BlcC8U5dp88CDFm9wZGVl8elbn9DVpSONvBpQz6cuD3h3Z+GkBXTu3YW9sfswmnK2dkjPTudAcghDXx1q0ZzulZSSkW+8TyfbJrT2aYS30ZU+7p1wybLHwWDHg76tMZ64zsL5C62dapE8O3QQm+JPkpyZs2eoUZpYf/UwDz71qNW34blfaDQaej31OMuvHMOYO4w9KSONFbHn6D90sMXjP9GvL5uTw7mWmgzkfKG0MfwMDdq3sshKyNWrV2fvlXPEZqTibLDFyWBLSnYWWy6f/k/1fpqkKPahmMXNT2QVq2WhKIqiKBZSbntAr6SkY9OuJ8PffsfisY4ePYqb0QV7/Y2hihqhoYo2CGcnZ3oO7cmCmQvQm3QIOw1vj32bJk2aFDteXFwcCQkJBAYGlnjV2+joaLJi0/H0zulRSk9Jw8PgTB2HqhyLPEsNjyCaetRi1eJl9H+uP5mZmYSFheHu7m7xRWhKonbt2rwzfhQ/fDkOojNJJ5veTz3Gy6+/au3U7ivDXhvBRAnjF/2NvdBisjfw+tefU6tWLYvH9vPz4/OJExj38WdkXU0k3WSkTY8uvDXyA4vE27t3Lw8EN2DvtWusOH8BDQIXe0cert6Anf/8wxN9+lgkblmjZkhbnbzNz4qiKIpSLpTbBmiV4Bq89t77pRJLp9NhKmTzAqPJiN6gZ8jQIbzw0gukpaVhb29f7PmUKSkpfPDmB5w9eAZ7rR3JmhTeHfUe3Xp0K3buWq02f+65qZmkRCNyGrfZ0ohOr+OvRX8y6ftfcdM4kpidSv3Wjfjs69HY2NgUO74lte/YgXYd2pOamoqtrW252qKmtGg0Goa/+TrDXnuV9PT0Er1+i6Nhw4bMXbGE1NRUDAYDOp3l/mTpdDr0Oh0jmncly2jEhMRGq2PN5VMWjVumSJCqJ9PaGgghEsn5a2yX+zO5t6WU0tl6qSmKoihKyZXbsYil+SG5bt26pNimEZ+ekFeWZczigrzIA71z1pHQaDQ4ODiUKK+Rb3+I6UgmD3h2o4NbW7o6dGLcyG84f/58sev09PTEtaInYYmRANg7OpCSlcah5NM08quJlJLd147RoEVjZnz9G097dqK3Vyv6VehC6t5Ivhn9VbFjlwYhBA4ODqrxWUJarbbEr9+SsLe3t3gjsEWLFpzMTiIhIw29VouNVkdKVgYhGbF06tzZorEV5V9SSq2U0llK6SSl1OX+/O9t1fhUFEVR7nvltgFamjQaDT9M+pFd2fvYFbuXfdcOsCZuA69/+iZ+fuZZbCgxMZGTB04S7FY9r8xGZ6C2vgbzZ80HICEhgSNHjhAXF1ekur/533gO2V5gQ8xuQnVRLIzfSIaTibD0GBZe3UyVbg0Iv3iF1i510WtvNAKaeNZkx7qtZGdnYzKZOHXqFGfPnrXKNifR0dEcOXKE5OTkUo9tbZcvX+bYsWNkZmaWeuzk5GSOHDlCdHR0qceWUnL27FlOnTpllq2WbGxs+OzHb/kl/DCLrxxjyZXjTLgcwttffYGz83/jc3/OHNDiH4qiKIqiKHdjtXFlQggtsB8Il1I+KIRwBxYAQcBF4Ckp5fXccz8EhgBG4DUp5VqrJH2L1NRUfvnxJ7as2YQQ0K13N5q1ao7JZKJp06Y4OjqaLVZycjK22oJbhzgaHLkWHcO4Md+waek6vPTuXMuKp0WXVnzy5ah76vmrUKECi1f9zaFDh4iPj2dc/fpER0dz9epVatasib+/P0P6DqSKTcV812mEBoPQsW3bNt544VXSr6eAACdvV2b/Pb9U5glmZGTw8dvvE3rgGD42jlxKj+fxQf0Z/FLZWOTJkmJjYxn52itor4XjZtBxOjmLF9//mO49S2f3hikTJ/HnjHn46925lp1EtSa1GfP9N6UyJPv8+fO8+/Ib2KYINAgS9Bl8OeEbGjRoUKJ6mzRtyuJNa9m3bx9Go5FRzZphZ2d39wsVpQwQQkwDHgSipZR1c8vuu/dWRVEUpXyz5sSm18nZiPzfroUPgI1Syq+FEB/k3n5fCFEb6AvUAfyADUKIYCml0RpJ/0tKySvPv4RbmIanPNsjkYQsPcSZE6f4feYUs8fz9fUl29ZIalZqvsWOLqSGUsmxCoeW7KaPb7e8IZL/bDzAtMApDH35pXuqX6PR0Lhx47zbnp6e1K5dO+92h55d2PPbGlr51s8ru56WiK2bA0Offp6OdvWp51cFk5QcSDzNY10f5PjlMxYf+vr9V+OwPx7Jy5XbADlzV/+YvoiqNarToWNHi8a2tk/feo3HbBJp3KwSAKlZ2Xz89WdUr1GTypUr3+XqktmyeTMbpv/N4IDOaETOQIrdR07y/dfj+XDUxxaNbTQaeWPIcHrY1sPLJ2fxrISMZD54+S3+3LAce3v7u9RwZwaDgTZt2pgj1fuSWs32vjYD+BmYdVPZffXeqiiKopR/VhmCK4QIAHoDN7fUHgFm5v48E3j0pvL5UsoMKWUocA5oXkqp3lZISAhZV5Jo4l0LrUaLTqOjuXdd4k9HcvLkSbPH02g0fDz2YzYlbON07FmuJkWyK2YPdjUcOXv0FK08G+abn9fcqz5LFywxW/wnn36S6xWMbIsMITwxiiMxZ1gZv5ugOlUJEt40cK6GRmjQabS0cK2NS4Ytf/31l9niF0ZKyfY1G2jrG5xXphGCnj61mD9t1h2uvP9FRUWRGX6Rxn7ueWX2eh2PV3Ri+WLLb5kzf9psOnrUyWt8AjT3qsGWVRssPgR73759eGfa42V/Yy9QFxtHqkpPNm/aZNHY/wWmEhyKdUkptwG3zsG4r95bFUVRlPLPWj2gPwLvATdv5ucjpbwKIKW8KoTwzi33B3bfdF5YblkBQogXgRcBAgMDzZxyfhfOn8fLWHAvQk9cCA0Ntcjw09atWzP97xksnreY6Mhonu82hK5du/Jo1weRtiZ2XQkhMjEaL0cPGvrWITszy2yx7ezsmLHgD1avWs3e7buoFNiYd/p9zejPvsBf71ng/ACDJxcuXDBb/MJIKdHInEbnzRz0tqQkRlg0trWlpKTgZCjYu+xio+d0QjwJCQn8uWAhZ4+eILh+HZ54+imzzmNMSkzCXp9/frNGaBAy5/dyu8WKzp8/z6I5C4iPvU7nXl3p0rVrkXvJU1JSsEFfoNwGHUlJyURERLB43gIiLoXRvF1rej/8oFmGBUdERPDn3HlEhoXRtF1bej1onnoVxcLuq/dWRVEUpfwr9R5QIcS/81MO3OslhZQV2sUipZwkpWwqpWzq5eVV7BzvRdVq1YjRJhYojyaeqlWrWixuQEAAb7z7BmO/G0vPnj3R6XQ0aNGE3/bMIS0mmZpUwhSXyeR9cwmqad49zA0GA488+ghjvvua4a+/ire3N90f6MHFzEhu/pVIJKEZUfTo0cOs8W+l0Wjwq1qJK4nX8pXvjb5Ax96WjW1tQUFBhGVpiE/Lv/DQxohEajRuxsBH+hA5fwP1LqUTPm8dAx7pQ2RkpNnid+ndg0Ox+VdfDk+KIaBqJTSawv+srF29ltf6vkjahot4HM9i/qjfGTH0lSIvINSkSRNCs6PJNmXnlZmkibPGKNzc3Xjhsf7ELT1CwGkjW35cwPNP9ic1NbXoD/ImBw8eZHifp9Fv3E2zsHgO/zqdoX2fKXG9ZY8o4aHcR8rke6uiKIpS/lljCG4b4GEhxEVgPtBZCDEbiBJCVADI/fffZTXDgJtXvwkArN691ahRIwyVXNgXdZxsk5FsUza7o47iWSuAGjVqlGou0miksVMtqtpVxEXrSCVbP5o51S2VMXG9e/fGVNGWHXFHSclKJSkzhY3XDuDfsDINGza0ePwPx3zOXwmn2BpxilOx4Sy/coQrXhr6Pdvf4rGtSaPR8NYXY/l4/xXWnotgX1gM3x24iKjRhH1bd/CAbQDt/KpT0dmDDn7BdDf48et3P5otfr/nniHax8S6qwc5GxfGrqgTrE09xkdjPyv0/KysLH78cjxPVmhPTc/KBDj70M2vGaknotm2dWuRYru6ujLojZeYH76VYzHnOXktlIXh23jg2ceY9r/feMKzBQ28q+Hv5EWnCg3wi9WzeMGiEj3ebz8ZxfBKdWjpF0iQizuPBtWgRlImfy9aXKJ6yxqJGoJbDt1X762KoihK+VfqDVAp5YdSygApZRA5CyBsklI+CywDBuaeNhBYmvvzMqCvEMJGCFEZqA7sLUkOWVlZ7Nixg/Xr15OYWLAX814IIfh1+u9UfrwRixO381fSTmr1bcWE338qSWrFcnjfIdrXa4WLrxsaZz3OPq60qtuMi2dCLR5bo9Gwbscmqj7TjEXG3SwR+2g+rAd/rl5612vPnTvHmjVrOH369G3PSUlJYdOmTWzbto2MjIwC91euXJnZK/+m1gsPk9KmMr0/eZlpC+b8J1YubdmqNT8vWkZWp6c5W6MDT3z+A19+P4ETBw9Ty8OP9PR0EhISSM9Ip66nP0f27jdbbDs7O2YsnE2fz16EjhVpPKw381b9ddvFj0JDQ/HAERudIV95sL0/29ZvLnL8p57py4QFk/F+vAEuD9Zk7MwJDHxhEOmxybjb5R8aX9ctiG1rNxY5xr+Sk5Mxxifhae+Qr7yltz871q0vdr1llZSi2IdSJpXae6uiKIqi3AtrroJ7q6+BhUKIIcBl4EkAKeVxIcRC4ASQDQwvySp9J0+e5KNXhlNTa8BOaJiYksiAt97g0T59ilyXnZ0db7z7Fm+8+1Zx0zELJ2dn0rLScXVzhdx1WTKyM7GxK535afb29nz17Td89e0393R+ZmYm7776JtFHL+CvdSbSlIRzsD/f//YTtrY3tppZv3Y9P4waS3WdJ0ZMfCPj+WLCNzRp2jRffc7OzuW+x/N2vL29GfzSsHxlNnZ2nD5/DptsMAgNcdJEhh5sXUu2OuytdDod3bp3p1v37nc919XVlRRjeoHyxIwUAn1qFit+lSpVGPHma3m3s7OzyZRZmKQp3+JICRnJuFcuOE/5XtnY2JBuMmKSMt9849i0VDyCvO9wpaKULiHEPKAj4CmECANGUUrvrYqiKIpyr6yyCu6/pJRbpJQP5v4cK6XsIqWsnvtv3E3njZFSVpVS1pBSri5uPJPJxCevvsYI/8r0r1qDx6tU59PaDZn7wwTCwsLM8ZCs4tmhA9h+LQSTzBkEZ5Im/ok5yJPPPW3lzAo3Y/JUdCdi6F+xNR396tI3oBXO51P47adf8s6Ji4vjh0/GMNivLd386tPTryH9PZvz6evvk5mZeYfaFRcfT9aFncRdb4+z3g53vT1rrpzAzc/Hajl5e3vjU6MiJ2Mv5pUlZ6ZxOOsijz75uFli6HQ62vbszK6ok3kr8WYYs9gaf5L+Lwy8y9W3p9frad61E+vDzufVm56dxd+RF3lqUPHrLZMkyBIcinVJKftJKStIKfW5I42mlsZ7q6IoiqIUhVUboKXt+PHjBCDwdnDMK9NrtXR28WDdypVWzKxkej/Um54vPMLi6A2si93Joqj1NHuqHc8MKJu9gmv/XkGbW3q9WvoEs2nFjT3QN23aRF29L7Y3Ddl0trEnSLiwd+9/Y5SYlJLExMQiN7hjLofj7+PHdxd3Miv8EN9d3ElFH3+unL1AVpb5VkYuqnE/fUdcNQ3zIjazJPIfliXvZdSEsfj5+d3xuqI8D+9+9AF2rYOYFr6ZPyN380f0dgZ/NIIGDRqUKPd3Pv6ItIY1GXP6ABNDj/HNxaM8++E71K9f/+4X32dkCQ5FURRFUZS7KUtDcC3OZDKhFQXb3BohMBrv3yU0hBC8MGwozw0aQFRUFJ6entjbm3e4pTkZTaYC23QIIfKthmoymtAWspWHBk2RV029Hx06dIgfx76HIfsaSelQt1kX3v5wdL4hyrcjJTxYvSk9qzYiPiOViwnRrDkTQpopi8c7dKPHE4/wypuv33a1Wktxdnbm56kTiYuLIyUlBX9//7vmsH//fr4cOZqshAwyTZm07tqWkZ99dNvtTwwGA6PHjSUxMZH4+Hj8/PzQ6Ur+Z85gMPDZuG/MXm9ZIwGTmsupKIqiKIoF/ad6QOvWrUuoMZP49LS8MqPJxJb4a3Tpef9v22FjY0NgYGCZbnwCdO7dnb2RZ/KVhUSfo123znm3O3bqyNHMSLJu2mojNSuD88ZYmjVrVlqpWkVkZCTjPhrMl73i+HWgLbNetCE4ew1jPn3nnq5v36Mre6LOY6PTk5yZzrazhxnoW5936nbmzaDWnF+ymcm/TLTwo7g9d3d3KlaseNfGZ3h4OB++/D6tTQ150LMLj3n1JHLzZT77cNRdYzg7OxMYGGj2RqKl6i1LVA+ooiiKoiiW9J9qgGq1Wj4aP45x50+y5MJZ1l44x5cnDvHAkMFUqWLePTOV2xv6yjCiAnT8GbaXPRGnWBKxnwseWQx/68aCMt7e3gx591WmXt7G9vDjbAk/yoyrO3h/7Khyv8Lt0j/n0b9ZJr5uOcOPhRA81tyJsNP/cP369bte//IbIzjnCQuuhDDv2HbauQaisTXg6e2NVqPhwYp1WD5/Ud58xrJq4ZwF1NFVw8kmZ8i8EIIG7rUJ2b6/2KtXK4qiKIqiKNZVfr/Gv40mTZsyd+0atmzZQlpqKv3atqVChQolrldKydK/lrD4j/mkpabR9aGeDBzyvNl7I7du2cKs36YTFxtLq45teeHlF3F3dzdrDEuzs7Nj6rxZHDx4kDNnzvBQlSo0bdq0QI/YY08+QbtOHdi2dSs6vZ5PO3bE2dnZSlmXnpjIy7T0Lvhfs4Kr4Pr167i5ud3xent7e6YtnMv+/fv5cPjrBHtXxdP1xjU6jRadCYxGY5nuyYu4EoGrIf/vWwiBo86e+Pj4/8RrwRrK+PcSiqIoiqLc58rup08LcnBwoHfv3matc/yXX3N8xS46e9bHxk5PyPydvLh5GzMXzUGr1ZolxvzZc1n84x909GyIi30wJ1afZdDm55j993ycnJzuXkEZIoSgcePGNG7c+I7neXp68vgTT5RSVmVDoxad2b5mPXUCb5SlZ5o4f01HxYoVb3/hTYQQNGvWjEeefpLzq/bma4DGpiVh7+lWphufAK06tGbp7kX4ON7Y6iTTmEmySLvrwkVK8Uig/M+wVhRFURTFmv5TQ3AtJTY2lu0rNtLbrwVONvYYtHpa+tTGNjKT7du2mSVGVlYWM36ZysP+bXC3c0Gr0VLPqzpV0tz5e/FfZomhlA3duvfgSGJ1ZmxOIDw2g8OhKbwzN4UBwz5Er9cXqa5nBz/PHk082yPOEpuWzLGYMGZFHOLtUR9ZKHvz6f1QbzL9TeyLPkR8egJXEsJZHbOF10e+UeYbz/czNQdUURRFURRLUg1QMzh37hz+ercCK7tW1Hlw7NBRs8SIjo7GRWOPTpP/g3clhwoc3nvQLDGUssFgMPDT5Pk4txzJhH01WBHdgVc+n0Pvhx4rcl1ubm5MWzwfnz4dWW8XT3zzIH6YO4MmTZuWOE+j0ciGDRsYP2YcC+bNJzk5ucR13szGxoZp82bQ5bVeXPCNRDaz5btZP9LrQfOOXlDyk4hiH4qiKIqiKHejuhHMwN/fn5jspALlMcZEmlSrbJYY7u7uJGSnYJImNDdtJROZeo0qNRqZJYZSdtjY2PBU3/481bfke7m6uLgw9JWX4ZWXzZBZjvT0dIY+OwR5ORN/vTdns48y45dp/DZnMpUqVTJbHDs7O/o/15/+z5XNPW0VRVEURVGUolE9oGYQEBBAxYbV2R11ApM0IaXk3PUwrtgm0a17d7PEsLOzo9tjD7AlMoQsY87WJFeTYzhsusyTzzxtlhiKcq/mzp6LzWVo592MKm6VaOJVjxbaeowe+bm1U1NKIGcf0OIfiqIoiqIod6MaoMWQkZFBSEgIp0+fztvK4psJ31Khdz1mRm9m2tUNxNTSMWnudGxsbMwW98333qbl891ZFL+dWVfXccr7Oj/N+g1vb+8C5yYmJrJv3z4uXbpktvjK3WVlZXHo0CGOHz+OyVR+l3NZv3wtddyC85VVcPLh0pnQcv24S1tcXBx79+4lPDy81GJKWfxDURRFURTlbtQQ3CJau3oNP43+hiC9M6mmLNJcbPhu0i/4+fnxzsj3eWfk+xaLrdFoeGHYi7ww7MU7njf1t8ksmjaXigYP4o2puFb15fuJE+67lXLvN3v27uH770dTv74fmZlGzp+7zqhR31C9enVrp2Z2jk6OpCenY6+/sSerSZqQggJzoZWik1Lyy68/snfvZho0DOT8uWicnQP4/LOvzPqlVqGx1VxORVEURVEsSDVAi+DKlSv8+tlXvFKpNba6nNVILybE8O7LrzF7yaIy8cF7586drJ6ymIEVu+TNFT165TyjP/qMcf/7zsrZlV+JiYn88MMX/DJxIG5uOQ39y5ej+eiDd/jjj7/MthVPWdF30DP88t4PdLNte+N1FnuaDj06lon/B/e71WtWERt3jGkzhuU9n4sW7uCXX3/krTct9yVXaRBC9AQmAFpgipTy60LO6Qj8COiBa1LKDrnlrsAUoC45I4YHSyl3CSEWADVyL3cF4qWUDS34MBRFURRFKSY1BLcIVvy9lDYOFfManwBBLl5oY1O4ePGi9RK7yaJZ82jjVjvfQkV13atwZHcImZmZVsysfNuwcT29H6yX1/gECAz0pn7DCoSEhFgxM8vo3Lkz3Z7vzd8x69keu48V0ZvQNXDk3Y/u78ZRWbFixWJefKlrvsb8E31as3v3VssGLsHw23sZgiuE0AK/AA8AtYF+Qojat5zjCvwKPCylrAM8edPdE4A1UsqaQAPgJICU8mkpZcPcRuefgNqbSlEURVHKKNUDWgSpScnYaQvuw2in0ZGWlmaFjApKTU7BVpd/TqgQAr3Qkp2djcFgKLVcjEYjv//8KysX/g1GI36VK/HeF5+UyyGpaWlpODnZFih3dLQtM68NcxJC8PKIl+k/sD/nz5/H19eXChUqWDRmVFQUX37yJcdCjiE0gg7dO/DuyHext7e3aNy72bN7F5N+/ILU+EiEwZEnnn2Zx5/sV6Ke4LS0NBwd87+eNBoNlu5cloCFZ/A2B85JKS8ACCHmA48AJ2465xngLynlZQApZXTuuc5Ae+D53PJMIN+3aiLnSX8K6GzRR6EoiqIoSrGpHtAi6NyrB/uSw/MWHgJIzkznKmnUqFHjDleWnu6P9OJQ/Ll8ZVEpcbgFeJf6B/XxX37FucWbeTWgNW9U7kibJBfeev4lYmJiSjWP0tCubXtWrjiSbwGetLQMdmw/S5MmTayYmWU5OzvTqFEjizc+MzMzGdx3MNpjGh707M0Dbj25vPYyb77ypkXj3s3x48f5bfRLjO5yjT8G2/Hbk5mELBnLX4vmlajedu26sGzp3nxlISHnqFTJ8l/eWHgRIn/gyk23w3LLbhYMuAkhtgghDgghBuSWVwFigOlCiINCiClCCIdbrm0HREkpzxb5gSuKoiiKUipUA7QIGjZsSM3ubZh+cQ8HI0PZEXGGSWF7+GDs52Vmjt/Djz6CCHZl1dU9nLwWys6oY6xJOcyocaNLNY+UlBT+Wb2Bnv510Wlynht/J3daGvxYNG9+qeZSGgIDA2nVqievvTqddetCWLFiL68Mm8qAAS/j4HDrZ2SlqDas34BLkjMVnSsCoBEaanvUIvx4GKGhoVbLa86UH3mziwZvl5yREfY2Wt5/wIE/Z/9aonr79X2WrVsi+P675Wzffozp0zbyw7cbeON1yw9xliU4AE8hxP6bjltXTCusD/fWpqsOaAL0BnoAnwghgnPLGwMTpZSNgBTgg1uu7QeUrPWvKIqiKIpFqSG4RSCE4MPPPuX4k8fZumETXi7OvPLQg3h6elo7tTx6vZ5fp09iz5497N2xi7qVKjKm1wM4OjqWah6xsbF4GBzyDUO8mnydg2HnCP/1KCaj5JmBz+Lu7l6qeVnS4EEv0r5dZzZtXo/BYODrr4bi5+dn7bTKhUsXLuEsXAqUOwtnIiIiqFy5shWygvAroVRrln+orL2NFrJSkFIWexiunZ0dE3+dxvYd2zlx7Aj+/m2ZOnU0dnZ2d7/Yuq5JKZve4f4woOJNtwOAiELOuSalTAFShBDbyJnvuR0Ik1LuyT1vMTc1QIUQOuBxchqviqIoiqKUUaoBWkRCCOrWrUvdunWtncptaTQaWrVqRatWrayWg6+vL9eyU8kyGtFrtZyJjeDPo9tp51KZByrUJXLZHgYvW8nUxXPx8PCwWp7mVq1aNapVq2btNMqd+o3rs2HWBqpz47mVUhJjirHqnOLaDZqz9/xy2ta4sfhUdEIWts4+JV4NWKvV0rFDRzp26FjCLItCYLLsNiz7gOpCiMpAONCXnDmfN1sK/JzboDQALYAfpJSRQogrQogaUsrTQBfyzx3tCpySUoZZ8gEoiqIoilIyagiuYhEGg4FnXxnK7Et7iE5JYMnJ3TzmVQ9/R3cq+lSgZYVqtMSLGZOmWjtV5T7QqlUrHKs6cjDmEBnZGSRlJPNP9D90eqgz3t7ed6/AQp4b8iq/7bJjx+kksrJNnAhL5aMlGQx9/ROr5VRiJRyDe8eqpcwGXgXWkrOC7UIp5XEhxDAhxLDcc04Ca4AjwF5ytmo5llvFCGCOEOII0BAYe1P1fVHDbxVFURSlzFM9oPex7OxsduzYQdjly9StX58GDRqUqT0Y+z77DP6BAcyeNI2ojEQ8PD3w8PJEq8n53qOeVyBz/tlt5SyV+4FGo2HSzEnMnjmbVUtWYWtry6A3BvPQww9ZNS8/Pz++n7KEWVN+Ytqfe/GvWJe3v3mjTI+QuJNSWAUXKeUqYNUtZb/dcns8ML6Qaw8BhQ7xlVI+b7YkFUVRFEWxGNUAvU9du3aNYc8OwjdZh6ewZ4NpIfbBFZgw6Vf0+oJbxVhLu/btade+PQ+164K7lye6mxZrikqJx7eimiOp3BsbGxuGvDiEIS8OsXYq+fj5+fHBp19ZOw1FURRFUZT7ghqCe5/66tMvaJXtSy//hjT3C+apgGbYnI1jwdyyOQLtsWf7sjzsMMbcbUpSsjJYee0UA18eauXMFEW5mYW3YVEURVEU5T9O9YDeQVpaGlevXsXb27vUV5G9EyklJw4cpnOlTvnKW3sFs/SvZTw7cMBtrrSe54cOITsriwlzF2GHFpOdnuFfjqRBgwZmqT8xMZFr164REBCAwWAwS533QkrJsWPH2L1rKy6uHvTo0QsXl4KrtZZHJpOJXbt2cXBvCBWrBNKjRw9sbW3vfqGZREREsGbVGozZRro/0J1KlSqVWuzMzEw2b9xI6NkzBNepS4eOHcvMVkwlpdqRiqIoiqJYkmqAFkJKyYxJE1mzaCZVXLRcTMimRdeHee29kWg0ZbfTOFsa0enKzvDbm2k0Gl589RVeeGUYaWlp2Nvbm2W+alZWFl+P+pQT/2zFz96Gi6mZPP3Syzz1zLNmyPrOpJSMHfMxsVd307WdO7GxWbw45Dc++OhHGjVqZPH41pSZmclLz79E8tkEKmi82Sd38/v3E5k8dyr+/v4Wj7/076X8OHoCFQlCg2D+pIU8N7w/zw953uKx4+LieHVAf+qSSXUHG7avXcrMX37il1mzy9QXVcUhAZNqgSqKoiiKYkGqAVqI1SuXc2blVCb3rIBGI5BS8tvepcye4c2Awbfuq176hBA079iWkL2hNPGuAuQ0hrZGneLRdwdZObs702g0ODg4mK2+X77/DufjexnfPBghBJlGI99M/oWAoCq0bt3abHEKs2fPHlLi9vDlBzcWf+rQOpX3x77PnHlryvSXFSU1f+58jGcyaO99Y6uf8MSrfDHyc36fOcmisZOSkvhxzAQ6unVDr835wqWqKZg/fplNj149qFChgkXj/zzuGx510NCqYhAALYF1F8KY+uuvvP7eexaNXRpU+1NRFEVRFEsqv5+QS+Dv2VN5qaknGk1Oo0IIwfONvVi9aI6VM7vh3U8+JNTHxIKwvWyMOMb0K/9QoVMDHn70EWunVmqklGxduYzHqgfkNQANWi0Dqvvz53TLb++ydfNKHuzmm68n18vDHn9vuHTpksXjW9PqJauo41YjX5m/cwXOHT+LyWTZdVT37duHN755jU8ArUaLr8mfbVu3WTQ2wJE9u2gR4JOvrFMlP3ZuXGfx2IqiKIqiKPc71QNaiJSkRNzs8/fS2em1mLIyLRYzJiaGX779nkO796K3seHhfk/Rf+CA2/aiOTo6MnXeLE6dOkVERAQ1atQgICDAYvmVRVJKdFLmbevyL3c7G66HxVo8vq2tPampWQXKU9OMpToX0hrs7OzITMzC/qYR3yZpQgosthVQQkICP47/kTXL13L1YiSZflk09m+KVpMz99IojNjaWf55Fxot2SYThpvmfGYYjdjY2BSrvlUrlrF45q+kJSdSqXptXnrzIypXrmyudItGLSakKIqiKIqFqR7QQjRp3ZFt5+PylR0JTyColmX29ktNTeXlZwbge/gy71VqynCvWhyevojxX46943VCCGrVqkWXLl3+c41PyBnO6+5fkcvxSfnKt16OpHXX7haP3+vBPsxbepXMTGNe2dGTMZg0XhYfBmptTw/sS0j8EUzyRm/nqetnaNulrUUaoNnZ2QzqN5iLa8J5wPMR2jp1JD46gQ1n1wKQlpVKlC6Czp07mz32rbo8/CjLz4fl3ZZSsujsFR54qm+R61owZwZbp33CmLYpzHjClj7uRxg5rC+RkZHmTLlIZAkORSlPmjesjZ+Xy12P5g1rWztVRVGU+4rqAS3EoJdH8OqAbUSnRtPAx44zseksC9MzfsqnFom3asUK6hhtaOhXEQBbnZ4ngury7ZoNxL/2Kq6urhaJWx68/cWXfPTiYHp7JlLJ2YGQawkc0jgw8TnLrwRcvXp1Hn78dV56bwKN6zkRez2b6Dg7vh7/u8VjW1v3Ht05ceQ4KxatwkfjSSJJ+Nb054NPP7RIvC1btiCitFRzDwYgKKgy2st6dsVvY0f0VjLt0vnqp7E4OTlZJP7NhrzyCp+FXuDTAyFUdbThTFI6wW3b8/Qz/YtUj8lk4q9ZvzPlMTcMupzvAusFODKwXjwL/pjK6+9+ZIn070gClh1ArSj3j7DwcCJG333NAr9PwkshG0VRlPJDNUAL4e7uzuQFy1ix9G9WHw2hUpta/Pb4kxZrCJ4/fooq9vnrFkLgb+NIeHi4aoDeQXBwMBMXL2HJooVsD71A/d6tGN67d6kNgX3k0Sfo1v0Bjh49iouLCzVq1DBLD2B2djYb169nx7oNuHp68vgzfa03LLMQQgjefO8tBr7wPGfOnMHX15egoCCz1J2amsrSv5eyf+d+gqoF8XT/pzlz8iwuuOedY2dnR/XgasRFRvPoiAcZMGAAen3prACt1+sZ8+MEwsPDuXLlCkMrV8bHx+fuF94iKSkJN5tsDDq7fOV1/OxZc+KoudItMjUEV1EURVEUS1IN0NtwcHDg6WeeBSy/nUf1enU4tD2EGh6+eWVSSq6kJ/0nh9YWlaenJy+8/IrV4tvb29OiRQuz1Wc0Gnn9hRdxunSVZm4+xJ8J572Vqxn2+Sd06dbNbHHMwd3dnZYtW5qtvsTERAY8OQDHWCf8bP04vPcIS+cv5dmXnyVB5B8WL4Qg0zaNBx54oNQanzfz9/cv0ZYzTk5OxGfqyMgyYaO/MRviaHgqVWqaZ39cRVEURVGUskbNAS0DHujdi5P6TA5EXcIkJSlZGSy8eJQ2D/bExcXF2ukppWzTxo04XIqgb5XaVHXzoImvP29Wa8DPY77CaDTevYL72MypM3GLdaOBRwO8HLyo4V6DZobmrF++HnxMnL1+GpM0kWnM5FDsARq0bYCfn5+10y4WjUbDk4Nf5Yt117mWlImUkoOXkvjjuIGnnxtstbzUHFBFURRFUSxJNUBvIqXk2LFjrFmzpsA2GlevXmXt2rWEhISYfZsJOzs7fpv7B/HNghl/+QCT487S4sX+vPXh+2aNo9wfdm7YRDNX73xl9noDPkJvle1dpJQcOnSItWvXEh5u2blOWzdso5pL9Xxl7vbuRFyMYOqcKdR4pAo70jexn510GdKBng/3YNOmTaSkpFg0L8h5HkJCQli7di0RERFmqfOJp/rT/aWv+GyPK4P+ymBlajO+mbSwWEN6zcUki38oiqIoiqLcjRqCmys5OZl3RzyPu/Ec1TwzWTnVgEf1jnwy+jt+/vYbDq1fRnMPLTsyBP/TuDL+9+l4eXmZLb6Hhwcfjf7cbPUp9y93by/ijl0oUJ6QnVnqPeJxcXEMe34Yxshs7I32xIgY2vRqy8eff2yR1W49PN1JupCEh84jr8xoMqLRaXBxceHDTz7kw08+ZN++fXww4kPcsjwRCEZrxvDRmA/p2r2r2XMCuHbtGq8NGopzQhaewoYpmddp3rsr73z0YYmfh+49e9G9Zy8zZVoyEjUHVFEURVEUy1IN0FwTxn9B78BT9GzkBNjxHPDzmnV8+fkoUg+s5/sOFfM+aIaEXWfMh+/w45SZVs1ZKZ8effop3vhrCbU9vHEy5OwtuTcyDN9awXh4eNzlavP6bORn+MZ4E+QeBOT0Au5Y8Q9rW62l5wM9zR5v4EsD+WL4aDrYdkCn0SGl5Mj1wzz49IN5//8yMjJ4/9UPaGnXAXsnewAyjZl8+eFYmjRrgpubm9nzGv3Bx7TLdqdWxZzhvl2kZO7KbWxq2ZwuXS3T6FUURVEURSmP1BDcXIf3bqJ7A8d8Zc+2c2D9X7N5KtgtXy9H4wA3Yi6cIjU1tbTTvGdSShISEsjKyrJ2KmaVlpZGcnKytdOwqIoVK/LWN2P5MfwUv5w/ylenD3C6ogdffPdtkeoxGo3Ex8ffdch4cnIyaWlpBcqzsrI4tv8YQS5BeWVCCOo61eHPOYuLlMu9at26NYPefZ6NSRv4J2EH666vpUbPGox4c0TeOZs3b8Ylyw17vT0mkwmTyYRBa8DH6M/mzZvNnlN6ejoXj5+ilvuNuaZCCDp5V2P5/OI/DxkZGSQmJpojRbNSc0AVRVEURbEk1QP6L2ni1pF0Wo1AmkxoCxlip9UIZBkdq7Z1yxYmfPkNujQjKaZMOj/0AG+8/w5ardbaqRXb9evXGTvyQ64cP4JOo8HOtwIjvxpXprYmMac27drRasM6wsLCcHJyKnKv3txZs5n9+wxspY50TTYDhg2m77PP5Dvn4sWLfPL2SGLDYjBJE9Ub1OSLcV/mxZJSIij42tcIDUaj5XaLfPqZp3msz2NERkbi7u6Oo2POF0MXLlxg5FsfEXrqElHh0Vy4EEpN+7rYaGzQG/SY7I1mn58Nd3oeRLEWhUpNTeWbUR9zat9O7HUapLMH7335DbVrl43N7NVcTkVRFEVRLEn1gOaq1bANO07m71lbtCuFdr37sORcfL7yk1EJOPlVxsHh7htUl7ZTp07xw3uf8ZxLfYYGtuK1Su2JWL2HCeO/s3ZqxSal5L2XX6JVQhjftqjJ182CGeBo4t0XBhXac1deaDQaAgMDi9z4XL50Oct+mkN/r/Y87dueZzza8/eE2axeuTrvnLS0NF4Z8BK1rvvRx6crT/l2x+kUjBjySt4XKwaDgap1qhKelH/hoROJJ3n4qYdL/gDvwGAwEBgYmNf4TE1N5cXnXsY7qhI9KjxKY0MbKlCJY4mHcNQ6Q4Zg75VdtG7d2uy52NnZ4VO1Ehfio/OV74i5QK8+jxW5vk/ffp3aUYf4pWMlvm0XyNtBgs9eHUpcXNzdL1YURVEURbnPqQZortff+5yZhwP4dmUKy/bE8cmfqZwyNuPzL79C1GnLxzuusPJEOL+HRDDhnJGRXxVtOGRpmTt1Jt3dgnE02AI5vTTd/euycekqsrOzrZxd8Zw5cwaH69E097+xMmxlNxdaO2jZuH69FTMrm2ZPmk43n6boNDkDHPRaHd28GzPrt6l552zcsBH/THd8HW8spFXNLZDMiBROnTqVV/bFN19wxu4ce2P3cjTqGJuubSGwbSUefsSyDdBbrV2zDrc0L9ztPUnPSMegM+CodSHbaOJw0n52pmylgnsAJ0+etEj8UePGsiLzMsvCjrDjymmmhO7BpWUdehRxHmxkZCTx547Ttap33rB+Pxd7evvoWL7kL0ukXiQSMJXgUBRFURRFuRs1BDeXu7s70+atYvfu3Vy5fJl+/WpTr149hBCM+mo8Z86c4WBICM19fHirbVurbHx/LyLDwqlrn38LD40Q2AkdaWlpODk5WSmz4ouJicHHUHD4sI+NjigzbYdRniQnJOHgZZuvzNFgR9K1G/MNo65G4oR9gWudhD0xMTHUqlULAF9fX/5e8zc7d+4kIiKCBg0a5N1XmiKvRmInc/LNzspGr9Gj0+sxCBtCM87TwK8pTjZORF6Nskh8Pz8/Fq5ezo4dO4iKjOSpxo0JDg4ucj2xsbFUsC/4Z9ffyYZD4WHmSFVRFEVRFKVMUw3Qm2g0mpwhfIUM4wsODi7WB87S1qJDW47N20g7v5p5ZYkZaeBomzec8X5Tu3Ztfk5Io7+UaG6aj7s/IZ2BLVtaMbOyqXqdmlw+F0UlF9+8sosJEdSsf2OOYdMWzVgzdSl1ufGaNkkT4dkx1K1bN199Op2O9u3bWz7xO2jRqjkrpq6mCsHY2dmRmpWKzqQnVSbT2aUX4XFXOMVRvmg60mI56HQ6OnbsWKI6qlatyqnELDKzjRh0N75U2RWdSuun2pYwQzOQahsWRVEURVEsSw3BLWee6t+Pk3apbI84RWxaEqdiw5kVvo83Py35foXW4u7uTpen+zEu5AznYuMJS0hm6vEL6GvUpUGDBtZOr8x548O32ZJ2nKMx54lPT+ZIzDm2ZZzhtfffyjunfv36BDSpwpbIvVxLvU5kcgyrrm7nof6P4u7ubsXsC9eoUSMqNw3kYNwe4tOuk2JM4lj6AfxsArDV2BNgqISHjSfp6enWTvWObG1teerFEXy26zInouKJSEhl1pEwIl0q0bFTJ2unB6hVcBVFURRFsSzVA1rOODk5MX3xPBbOm8/Wrf/gWyuA74Z8QLVq1QqcGx4ezqply0hPTaPLAz3LxCqcUkoOHjzIPxs34eTmSq+HH8bb25uhw0fwT8PGrJg/l4z0NLq8/Bw9e/W6bxvVlhQUFMT0v+cye9osQo6dpHq7mgyp3ouFf8wnsGogvR58EEdHR777+QdWrVzJqj9XYGNry9vPfUSbNm3uOY7RaGTz5s2E7AkhsEolHnyod6G97JcvX2bF0hVkZ2XT88GexRpJIIRgwsQfWblyJZN+msJl3UUa+jbFLssRKbPxdvckO6Mmx48dp2HDhkWuvzQ92e8ZqtaoyZLZM0hKTKDt088y/NFHy8gq1RKT6gJVFEVRFMWCRFndSqSkmjZtKvfv32/tNMqsNStXMenLr+jk7I2NRsc/idE0evgB3nj/PavlJKVk1PvvE7NnP61c3EnMzmJTQizvfTuelq1aWS2v+1l6ejovDRiC7koaQQZvYrOTOKuJ4Zc/JhEUFFSieoc8O4T00Ey8tT4kGhOJsY1i8txJVKpUKe+8Pxf9ya9fTSRIVEEjNFw0XuDxwY/x8oiXix37xIkTvDvgQ5q7tMtXfvD6Ht753+u0bVsGhrKWEiHEASllU3PVV8EpQA5s8nqxr/9m63tmzUcpe/5L761+Xi5EjL77avd+n6QQEZNQChkpiqKUbff6uUT1gP4HpaWlMXHs17xXvTF2upzFlJr4BvDjstWceexRq8113b17N9f3HODVmvXyyhp7+zJu5Mcs3rgejUaNGC+qBXPn43LFSJsKN/4WBCbF8OXIz5gyd0ax6507ey7ygoZmni3yyqKTo/j8w8+ZNncaAImJifz89S90ce+etyJvZVmFP6f/xUOPPURAQECxYteqVQuPqq6cO3eKKi7BCASXE0MRPkaLbMPyX6KG0iqKoiiKYmnqE70FXbhwgf3795OammrtVPI5cuQINWyd8hqfkDPEsYWjB1s3brRo7LCwMPbt20d8fHyB+7auWUNbD698ZS42tvhptFy4cMGieZVXG1asoYFH/uHX/k5ehJ29iNFoLHa9a5etpbpL/i8qvB19CD19EZMpZ0OO/fv3441PXuMTQCM0VDD5s33r9mLHFkIwcdqvVOkZwLbktWxJWo1nOyemzpmivqQwAymLfyiKoiiKotyN6gG1gLi4OEa+9hK2CWH42mkYfy2bPkNH8GS/Z62dGgB2dnakmQo2PtJM2Xg4OVskZlpaGh+89iYxJ87jo3cgNCOerk8+yvA3X8+bx+ng5ERKVlaBa1Oys7G3L7hliHJ3Dg4OpKVm4Gi48fyZpAmToETzZx2dnMi4no6d3i5fvVIj8+q1t7cnW1Nw79lsTTb2jiX7fTo4OPD5mM9gTImqUQohVR+ooiiKoigWpLoLLOCL99+kj0s0o9v5MLypF7929WHD9B85duyYtVMDoG7dukTbarmccD2vLCkjnX9S4ujR6wGLxPxuzNd4X0jgxaCWPOJfj9cqt+XgotVsWLc+75yH+vRhbVwUadk3GqEnY2PQVvDGz8/PInmVd30HP8uO2OOYpCmvLCTmDO26dyxRb+Ezg/txNOlwvnrPXD9Fpx4d8xqgzZo1I8Uumfi0G6+zlMwUonRX6dKlS7FjK5ZlKsGhKIqiKIpyN6oBambx8fFcv3SKFpXc8soMOg3P1HRg2YLZVszsBo1Gw/jfJzI/LYrfLxxh5sUT/HDlOO+NG2uRLTiklOzasJlWPlVv5CAEPX1rsnjmjeckKCiIQSNH8uWZY8w4f5oJZ46zUmYy9n//M3tO/xUdO3Wi08CHmBWxiXXRB5gfsZXMOs6889H7Jaq3c+fO9B7Smw3X17I/fi9brm/AqbED7398o16tVsvP037imOEIu+P/YU/8TvZm72Tcr9/ct3vSKiUnhOgphDgthDgnhPjgDuc1E0IYhRB9binXCiEOCiFWFHLNO0IIKYTwtETuiqIoiqKUnBqCa2aZmZk46Aq26x0NOtJSkqyQUeECAwOZt3I5Z8+eJT09ndq1a6PTFXw5bFy/kd++nUBqfBK2zvYMef0Vej3Y+7b1pqam8sPX49mxbjNISc2G9Xjvs4/QINDcMuTTTmcoMD+2Z+9edO7WlZMnT5KVlcXiWX/wwiOPgdDQrGN73hz5oVkbL6mpqfxv3Fj2bV2PkJJq9Rrz1idf4O3tbbYY1vbSqy/Tb0B/zp8/j4+Pj1l6k4UQvDLiZZ4dmFOvr68vFSpUKHBetWrVWL5hGadPnyY7O5tatWqVke1GyqZVy5cxe+L/MKYlYevszuA33qVDp86lFt/SixAJIbTAL0A3IAzYJ4RYJqU8Uch53wBrC6nmdeAk4HzLNRVz671sgdQVRVEURTET1QA1My8vL5L0zlxNTKOC8435casuJNPh5Uetl1ghhBB3XPF2186d/DJyLH38m+PkYk9qVjozPv8Rvd5Atx7dCr3m3eFv4BmayvDADmiEhrOhEbzy3BD8q1XmwvVoqrjeaNjtjrpA14EPFqjDYDBQt25d+j/0CL30TvSr0xwJ7Dx0irdeHMbvc/4w2/6fH4x4idbGUF7q5ocQEBJ2ijcGPcOMv1dhMBjMEqMscHZ2plGjRlapVwhBzZo1zR67vFm7eiWrfxrN1839cbZ143pqJl9++T62dj/RomXLUsvDwltzNQfOSSkvAAgh5gOPACduOW8E8CfQ7OZCIUQA0Juc2b9v3XLND8B7wFLzp60oiqIoirmoIbhmJoTgg7Hf8/GuRP48Fs2OC9cYs+MqKRWb0Pk+m/c2ZcJEHvJphFPuAjb2else8WvCtJ9+K/T80NBQ4k5fpoVPdTQi56VV3c2PSul2tOnWmWWpoawPP8mR6MssvnyIq362PN2/X6F1bd++naAMIw29KyBETu9pW/9KaK9GcerUKbM8vnPnziEiz/FATW80GoEQgiYV3WnikMKWzZvNEkNR7tW833/mraZ+ONvmrE7tZm/gjUbezJ5YukPQSzgH1FMIsf+m48VbqvcHrtx0Oyy3LI8Qwh94DCjsD82P5DQyTbdc8zAQLqU8XKQHqyiKoihKqVM9oBZQp04dJv+1hjUrVxAafZU+L3SkSZMmZuu1Ky0xkVF4eFfPV+Zs40BSeHyh54eHh+OtKzg81kfnSFpyCnNXLmHt6jWEhV7kyWZNaNeu3W0Xwgm7fBl/nU2Bcj+tDeHh4dSqVavoD+gWERERVHEqGL+Ko4aIKxdLXL9iXtnZ2SxbsowVf63E1s6Wpwc8RYcOHaydltmkJyfiZp9/eHSAix0xRyNKNY8SdoBeu8sG1IX9Ebw14o/A+1JK481/M4UQDwLRUsoDQoiON5XbAx8B3YuZs6IoiqIopUg1QC3ExcWFp5/pb+00SqRqzWAuXYwmyMUnrywyOY4KlSoWen5wcDAXM+KQUuZrbF/Ivk7Hxg2xt7fnsScev6fYderVY2rGH9zcvJBSciYjhUFmGs4ZHBzM9NjsAvmGxJl4qL75h6sqxSelZMRLrxEVco1qTjXIMmbx9RvjOTLgCCPeHGHt9MzC3defy/HJBLo65JUdi0ygWu26VszK7MKAm/+ABAC3trCbAvNz/096Ar2EENlAC+BhIUQvwBZwFkLMJmeuaGXgcO41AUCIEKK5lDLSkg9GURRFUZSiU0Nwldt69b03WR1/jLNx4RhNRi7EX2XJtYO89uHbhZ7v7e1N695d+PvyfuLTk0nLymBz+HFMFV1o0aJFkWI3bNgQUaUSf184TXJmBvHpacw5d5zqbVsREBBgjoeHr68vtdv15H97wrmWnEFSRhZzD13lultVmjVrdvcKlFITEhJC2KEIGns2x9nGBQ97T1p5tOev2UuIi4uzdnpm8dK7H/H1gWsci4wn22jiQFgcP59IYvCIW6c6Wo4s4XEP9gHVhRCVhRAGoC+wLF8OUlaWUgZJKYOAxcArUsolUsoPpZQBueV9gU1SymellEellN43XRMGNFaNT0VRFEUpm0q9BzR3pcJZgC8583gmSSknCCHcgQVAEHAReEpKeT33mg+BIYAReE1KWdjKiP9ply9fZtbvEzlz9CiVg2sw4OVXqFq16t0vvIOqVavy64IZTPlpIjuPHqBqjer8MGJSgYWLsrOzWTR/Iav/XIpWq8GvY33Wnb1ARkYm3Qb2ov+A54q856QQgu9++5WFc+fy299L0el0PPjqizz6+L31oN6rtz8exYplDRi/cA4Z6el0enAI3z874J6HSycnJzN35nR2bVyHs6sbfZ5/gXbt25sltwsXLrDrn524urnSqXNn7O3tC5yzfds2Zk+eScL1eDr06MJzgwaU2hYn0dHRTP51Mgd276diUCAvjniROnXqWCTW3l178eRGT7yUkoT4BNLDMnny4ScZMmwIT/V9qtCVnC3h/PnzTPppEmdOnaFOg7q8OHwogYGBJaqzQYMGfPb7bGZN/B+/7T9DcJ1GjJsxgkqVKpkp63sgwWTBRYiklNlCiFfJWd1WC0yTUh4XQgzLvb/wCeZKiQkhLgJJ5LyPZkspm97pffd+1LxhbcLCw+96XoC/P3sP3brulaIoilJahIVXPCwYUIgKQAUpZYgQwgk4ADwKPA/ESSm/zt0bzk1K+b4QojYwj5zVE/2ADUCwlNJ4pzhNmzaV+/fvt+AjKTtCQ0N59/nneNbPnTpeHpyJvc7My9F8OnESdetadvielJI3XnoFzfEo2nhXxyhNbI46RYVODRk1drRFY1tbRkYGQ/s+QUd9Cp2CvIlLy2D6yUjaDHiZfgMGFrteKSXfjvmafSs2Us/gRRKZnJTxjJ/8S77VZOfM/IO/f5pDR8/6OBkcOBJ7niuuyfzx1zxsbArOnzWn6Ohonnv8OapmVaaScyVi0+IISTnIqAmjaNe+ndnjLV26lDmfL6S+R87Q6PCwcNIS0zmSdoCW1VsRkxmFVxMPfpn8s8XnWh87dozXnn+devp6+Dj6Ep4Yzil5gikLplC5cmWLxr6VEOLAXeZcFom3Y4Ds03B4sa+f+M9Is+ajmE9uA7SplPLaTWXjKOR99071lOX3Vj8vFyJGO9z1PLe3orBzuPsXdSnJyST84HPX8/w+SSEiJuGeclQURSnP7vVzSakPwZVSXpVShuT+nETOfm7+5CzFPzP3tJnkNErJLZ8vpcyQUoYC58hpjCq5Jv3wPS8EetG4gjc2Oi31fDx5rXoAv37zlcVjHz16lPhjl+hZsQFONva42jryaGATjm7eRVhYmMXjW9PqlStoRBIPBvvhYNBR0cWBkc0r89e030lPTy92vfv37+foyi28WLk1rQOC6RFQl2c96vPx6+/kbZGRnp7O7InTeTygLV72btjqDDT3qYXndQOrlq8010O8rSkTp1A1qzLV3Kqh1+rxdfShg1s7vh/zvUXide/enXj7a1xLiSEzM5PkxP+3d9dhcpVnA8bvZyXuSgSCBQkBAgS3AEWLFy3loxQtUEqRAhVKkQIVKLRIcXcvxSlSNFiwBAkaI0JCnGx29/3+mEnYJJvsJpmd2dm9f7n22pl3jjznZHaeec55z3tmMqVyMqUtS+jbcUU26D6Yr4aN4r33fnkhggAAbaZJREFU3muQ9df01/P+ysatN6FPh76UlZTRr1M/1i1dj8v+kt/RaqUcWFzebdIqq6sZe27bOn+qU3XdC5MkLbWCXgMaESsDGwCvAT1TSuMgU6QC824YWeew/c3dp8M/YO1uXRZoW6lTByaOHrWYOXLnvXfeZZXSjgu0RQSrlndm+PCm3cXpvaGvsmH3BY+il5WUsHK78mUuvisqKrj9hpsZ1GaFBc7kdWvTgdYzqxg1KvN/OmrUKLqWdqC0pHSB+Xu16MQzjz9FRUXFMq2/pkmTJvH5559TXb3ol7C3h77NSh0W7HLatkVbpn8zjaqqJXZOWMDUqVP57LPP6oy3devWXHPb1UzqPZbHv36IF6c9w5SWE9l17e/vI9u5sivvvdvwBeiYL8fQtU3XBdp6te/NiPdGNPi686GBrwFV4STgyYh4s8btcRaXdyVJajAFGwU3ItqRudH4SSmlaUvoNlefYfvnLfNo4Ghgua/HKiZde/bk6xmz6NX++65H3343h9btOzT4ulfstxKvpNmLtE+omkWfPk37OEGfVVbjyy/eZO0enea3pZQYO7OC7t27L/Xy7rnjTm76x7+Ib2bw+pQpvDf+Cw5cZ2vKSzNFZmWqmn+NY48ePZhSOX3+CL6V1ZU8MOI5Rk4YRY9xPdljm1058qRj2P+gA5Y6jqlTp3LaiacyesRXtC5tzazy2Zx57m/ZZtvvr21dceUVmTRsEr3bf3/bkLlVcyltWVqv630rKio464yzeOt/b9KutB3TmMbPTzmOH+3/o8XO069fP2699xbeeOMNzj7mHLbouuC1trNKZ7LiSrWP0JxL7Tq2Y1bFLNqUf39N7tQ5U+nRq+6ueo1dIlFtKdlUbZlSGhsRPYCnIqLeN1RurrlVktQwCnIGNCLKyRSft6WU7s82j89eHzrvOtEJ2fb6DNsPQErp6pTS4JTS4GUpAIrV/x3/C6766EumzcmcRZpZMZerRnzGIcce1+Dr3nLLLfm6XRUfTh5NSomUEm9N+Izo05EBAwY0+PoLae/9D+CRCRV8Pnk6AFXV1dz94VjW3mJbOnbsWMfcC3rttde47+/X8IsVt+DYdbbjsD4b0XFO8PBHrwHw1bRJlPXoRO/emYKvY8eObDRkc14c/x7VqZpHP3mZ8hml7NZ9Kw5YfVf277Y9t/zlWl5//fWl3q7TfnEqbT8pZ7duP2C7zluyfcutOPeUPzKmxuAeR51wFMNmv8v0OTOATPH56jevcciRP6nXNZgXnHMB41/8ml277czWnbdk5447cs0FV/PWW2/VOe9GG21Ex5Xb89mUT+a/50ZN/Yq5Xeew5ZZbLvX2Lq2jf3E0r015lTmVcwCYPXc2b0wdyrEnHdPg686Heft0WX7UeKWUxmZ/TwAeIHMpy+Ly7sLzNsvcKklqGHkvQCPz7fQ6YERKqeYFYw8D80ZuOQx4qEb7QRHRMiJWAfoDQ/MVbzHYfIst+MnvzuZPn43n1DdGcPbHY9j1l6ex8267Nfi6S0tLufKW6/lqtVZc9sWzXPrFf5m54Qpceu1VDT4YTKF16dKF8/91AzdNb8vx//uU41/+krTZrpx+9tIPvnTHtTeyW4+1KC8tpXWrVnTq0Y212vXgpVEfcvdXb/JIxZdc8I8Fr6/8/Xl/YNU9N+KGsU/zyuj3WKPTqqy0ysqUlZZSVlLG5h3X49ZrblrMGms3YcIExnw4itU6fT+YTuvyVqxZuir33XXf/La1116bc/95Lu+0epdHJz3Gf2c/xz4n7suhPz20znVUVlbywhPPM7DL9wcoykrKWL/tetxyzc11zh8RXHnDlXTdujNPTvkPT015lLJBcN1t11JaWlrn/Mtr1x/uyjG/P5pXql7iicmP80YM5bSLTmOLLbZo8HXng11wm56IaJsd9I+IaAvsBLzP4vOuJEkNphBdcLcEDgXei4hh2bbfABcCd0fEEcBXwP4A2SH67waGA5XA8XWNgNsc7bDTTuyw005UV1cv0AVywoQJXPHXv/He0Ncpa9mCPQ8+iIP/7/+W+rYoS9K9e3f+duU/5p8BaeqFZ039+/fnilvuWGS/L61vv5lMp5bf326ja9eudOrUiW5zP+dnf/s9G2+88SLLLy8v5+QzTuWEk09kz612ZZUVFhyBtX2LtgyfVGtngcWaNm0arUtaLdLetqwt30yYuEDb5ltszv2P3b/Eba+oqOBfl/+LRx94lOrqarbdcVuOOu4oSimjJBacp12Ltoz85rN6xdmhQwcuuvjCgr3n9t53b/bed+9Ftn3MmDFcfMHf+ODt92nTrg0HH34I+x24X7P6m1Cj1BN4IPs+LANuTyk9HhGvU0velSSpIeW9AE0pvUjt13UC7LCYec4Hzm+woJqQml+GZ82axfGH/IQ92nRkvzUGMruykgduvp2/jR7Nab/7Xc7X3Zy/ZC9vQb/1Tjvw1p3PsE3vNee3TfpuOn3XXI1NN910ifO2aNGCLr268c3Mb+naptP89hFTPmXIvrX+SS3WyiuvzLSSmcypnEPLsu9v5fLFnK84dudf1DrPkrb9pONOYvqw6Qzpsi0lUcJH//mYX7z9Czr26Mi3s6bSqdX3XZVHTv2U7Q5ZungL/Z6rue1TpkzhyIN+xqC0Jnt1+gHfVc7h7r/exqRJk/j5CT8vYJT1l8BrQJuglNJnwPq1tH/DYvKuJEkNpaCj4Kph/eff/2ajkhZstEJvIoI25eX8ePW1eO2xJ5g61XuWNSYHHXoIH3eYyxNjPuCLqRN57euR3D7xPc447w/1mv93F/yBJ2cM5d1JHzNm+nheHP82k3rO4eCfHLxUcZSVlXH6OWfw+DfP8vHkTxkzbRz/m/AqXdfvybbbbrtUy/r000/56p2vGNRtfcpKMmc81+6yFpVj5rLPIfvw0syX+fCbj/h6xnjemPgm3/WZwwHLMGhSY3HXbXey6tw+9Ou0IhFB6/JWbNdzcx645V7mzJlT6PDqLS3HP0mSpLoUbBTcfHjmv8/w0kv/pW3b9uyx+76sscYahQ4pr0Z+8AH927VfoC0i6NemLWPGjFnqgXKaqvfff5//PHg7cyu+Y8hO+7Llllvm/cxa27ZtueGeO3jsP4/y5kuv0neVdbn+wP3rPZrugAEDuOXhO7j/rvv46vMv2X3LA9l1991o2bJl3TMvZIcf7MDq96/OPXfcw5RJkzl61+PYbrvt6n2WN6XEiy++yBWXXk7lxLnMbjOb1q1bz3+9Y3UnqiqruP2RO7j3jnsY9eUo9t/6QHb74W60aNGi3nG+88473HvHfVRVVrLnfnuy6aabFvSM6EfvfcgKbRa8i0VJlNC+pC3ffPMN48eP5+G776eysoo99t+bjTfeuOBncGtjISktndkzZ9C7e935tG+fPgwd1rRvTyZJ9dFkC9AxY0Yx9PW72XvfDZg6dSZ//suv2WefI/jhbnsUOrS86T9wICNfe4u1u33/pTilxJezZ9G3b98CRtZ43HHr9bz48KX8ZNtyWrUo4f4b/ssLz+zCb/5wYd5jadmyJXvvuw9777vPMs3fo0cPjv1Fbrp69uvXj1PPOHWZ5j3rzN/zwVPv0iO68dHUjxj9+Si69OxK166Ze2d+W/Ita6y5Bj169OC4Xx6/TOu4+oqrue+aB1itxRqUlpRy7jPns8Vem/Hbs3+7TMvLhQEbrMPbw16he417hFanaqZXz+SBu+/j2dv/w0ZtV6NVBH97/mwG77Utp/329ILFKyk3KqurGXtu2zqn6/37MXVOI0nNQZPtghslVfz69L1ZZ51+bLHFAC79x+Hccsu/iqor3PLabffdeYtKXh83huqUmFlRwS2fjGCLH+5Khw4Nf4/Qxm7q1Kk8cvflXHxkZzZZqwPrrdqOPxzcmYkfP8GHH9b7FnmqYfjw4Qx7+m2G9NiKdXqsTecOHfhqzigmjB9PxdwKPpj0AS1XbMnGG2+8zOv45ptvuPO6u9m623b07bgivdr3Zotu2/D8v//H559/nsOtWTr7H3QAn7ccy2dTviSlxMy5s/jv1y+xy49249HbH+RHfbZi1c59WLlTb/bpsyUvPfwMX331VcHirV3mPqDL+iNJklSXJluAdujQeoHnrVq1YN11e/PRRx8VKKL8a9OmDVfefhtj1lubsz95n79PGMWGR/6Uk05f8lmXlBLvvvsujz/++CJfkMeOHcsTTzzB22+/XfT3/Xv33XfZvH+irPT7P4OIYIeB1bz2yv8KGFlufPzxxzz++ON88skneVvnSy+8xIrRe37X0t3X3oWO3dry3LTneeSb/zBgv3W49tZrl2vQprfeeoseqecCo+hGBD2rV+DVl19d7m1YVp06deK6O2+gZJO2PDz1GV4peYdDf/czBqy3DquWdScIZsyYwfTp00kpsVpJd157tXDx1iYBKdIy/0iSJNWlyXbBrZy76J1aJkyYTufOnQsQTeF07dqV355X//tSTps2jV/87BhKxk2ne7TmuspvGbDtxpz1p3P5y/kX8Pqjz7FaWWemMIeZXcr5543X0KVLlwbcgobTqVMnJk5btBCaOC3osna3AkSUG3PmzOFXx57INx+Opmd0YHyaRo+B/fjb5X9fqmssl0XXbl2Zzez5z8tKytii36bMbV/FH687j3XWWWe519G5c2fmlC7ak2FOyRy6dCvse7F3795ceMlFC7S99tprTJz1LSM/+oQW2Y/crxnHhPIpdOnatbbFFJRnMiVJUkNqsmdAJ0+ZyaRJ34/0+vLLI6isbMOKK65YwKgav4v+eD5rTWnJgX03Zvs+Azmi31ZM/N8HnPvHc/j4sZc5cqWt2L7PQH7UZyM2nt2Fs3/9m0KHvMwGDhzIV9O78+5nM+a3jZk0h8ffb8kPdtypgJEtn39dfhUtP57Bvr22ZMsV1mXfXlsSH3zLtVdd3eDr3mmXnRhd9jVTv/v+b2/M9HHQtYQBAwbkZB0bbrghlZ0qmDBj/Py2b2dPYUrLSQwZMiQn68ilgQMHMvSr95lTOZd25W1oV96G6urES1+9w/rrL3JnjIJzFFxJktSQmuwZ0BV69uGUk+6i5wptmTZtFu3arcB55/610GHlREVFBd999x3t27fP6SiaKSXeevEVTuy3/QLt23Rfk7/cdjcnrr3zAutbu0tfnnn/OSoqKhr8zFpDiAguvPRGzj7zeHj6C1qWB1MqOnH2ny+jbdu6B5RorJ56+DEO6r7FAm2bdF+Lex58lONOPKFB192uXTsuve4yzvzlGaTJ1VSlKrr268bl/7giZ+/VkpISrrrxSk494TQ+HP0+QQmtu7XiisuuWKZRf5dVVVUV06dPp0OHDkvsUjx06FA26TeIFyZ+SMm0RBBUlFSz2Uob8vLLL7Pnnnsu03IlSZKKUZMtQNu0acMtt9zPmDFjaNOmTdF2E62poqKCSy46m2EvPUGHVonZJZ35xZkXsPHGm+RwLYsWCaURVFenBa65mz91gurq6hyuP7969uzJldffy8SJE5k7dy69evVqlLfGWBqpuppY6P8xIvL2/zRgwAAefPIhxo0bR3l5eb1vJbM0+vTpwx0P3M6ECROorKzM6/9bSombrr+JW6+5lfLUgqqySo468Sj2P3D/Wqevrq6mfct27LbRAUybM4PqVE2nVh14bdy7VFdVLbDcW2+4jgdvvomOpcG0VMIhxx3PPvvVvtyG4plMSZLUkJpsAQqZL91N6XYjfznvt/Sb/hgn/zRz5nPitGmcdtYxXHD1wznpWhwRDBw8iBEfj2ZAl++X99LET9hx3915+ZWP2avNRvPbP//2a/quuSqtWrVa7nUXWkMUSYWy7S478M5jw9mw+5rz24ZN+oTt99wxbzFEBL17927w9fTo0aPuiXLsvnvu475/3s/23X5AWUkZc6vmcv0F19O1W1e232H7RabfbLPNuCidxyaVFXRo2Q6Aiqq5fFI9lnO33Xb+dPffczfv3H4Tf9lgdcpLS/iuspK//fMSOnfpypDtF11uQ0iRqI7iPaAkSZIaP/t3FYkZM2bw4RvPcOCm33e77d6hnJ9uPJcH7ro5Z+s545yzeLVsIg+NfotXxnzI7aNfI62zAudd8Cc6b7omN331Mi+PGcEjY4bxVPWXnHXReTlbt3Lj+JN+wbiec3n066G8MW4E/xn3GhN6V3HMCbm5R2hzd9PVN7Nxl00oK8kcvysvLWdwp0249p/X1jp927ZtOf2833HvxGd55et3eG3cu9wz/r+c8LuTFuiZcd8N13PEgFUoz47K3KqsjKPW7sftV1/Z8BtVg7dhkSRJDalJnwFtSqZNm0aPDrFIN8O+Xct5Pof3EuzatSt3PHI/r7zyCqNHj2b/gQNZZ511iAj+dMlf+Oijj3j77bdZYYUV2GqrrSgr8y3U2LRp04ab7rmNN954g08//ZTVV1+djTbaqOi7FjcW382cTasOC571b9eiHZMnTV7sPDvsuAODNxnMc88+S3V1Nedsuy1dFxoBd+7sWbRrUb5AW7c2rfl25Bc5i70+Ep4BlSRJDcfqoUj07NmTMdNbMPO7Ktq2Kp3f/r9P5jJoSG6755WWlrLVVlvV+tqaa67JmmuuWetrajwigo033piNN9640KE0OauusSrjR46nZ7ue89tGTR3FoC0HLXG+jh07stfeey/29V6rrsank6eyWpeO89veHjeRgRv5fyhJkpoOu+AWidLSUo448SxOvXc2b30+g7GTK7jlxam8+s3K/HD3vQodntRsnPb70xhW+TafThnJ9DnT+Xjyx3xc9iEnnnrici33F2f+ln9++jUvj/qaiTNn8+yXY7lt/HSOPPGXOYq8bpmbqSx7J1xJkqS6eAa0iOy48670WbEf991+DVO++JrBW+7MP/c7sEkMAtScTJ8+ncf+8wjjvvyS9TbehG223ZbS0tK6Zywiw4cP56nHnqRV61b8cM/dczYY2PDhw3ny0cxy99h7D/r06bNMy0kpMXToUF589kW69ejGHnvvUe+RsldffXVueeBmbr7+Zj4Z/gnrDBrIBYefv9wDWfXv35/L7ribO2+6gZc++pA1NtuBKw/7Kd26dVuu5S6t6vBaTkmS1HAsQIvMgAEDGHDeJYUOQ8vo888/57QjDmfbDuX0a9OKZ59/gjuvu4bLrr8xr/ewbEiX/uViXrjncdZrtSIV1VUcc8PdHHfWyfxwj92Xa7mX/PlinrrrKVYtXZm5aS73XncPp5xzKrvuvutSLae6uppfHf8rvnz9S/qU9OG96ve5+aqb+dvVf2PQoEH1Wkbv3r0543dnLMNW1L3ck8/8bc6XuzQ8kylJkhqSBaiUR3/+/W/5xcrdWDV7nd9GvXtwz0dfcM+dd/CTw35a2OBy4NNPP+W5ex7jx323mX/f2HWqVuaf5/+N7XbYnjZt2izTckeOHMmTdz3Jzt13mD+Y0upVq/HXc/7CkB2G0Lp163ov65lnnmH00NFs2e3765z7zenH707+Hf9+5t8O1iRJktSAvAZUypPKykomffnF/OJznh1X6sULj/4n5+ubMGECQ4cOZcKECTlf9uI8/9/nGFDea37xCdCytJx+Zd0YNmzYMi/32aefpV/0XaA4bFFaTg+6L/Vyn/z3U6zSatUF2tq1bAczg3Hjxi1zjA1h8uTJDB06lDFjxuRlfV4DKkmSGppnQKU8KSkpoTIlqlOipEYhNa2igrbt2udsPVVVVZxz5u95/39D6d2iA2MqprLetptx1p/OpaSkYY85tW3Xlu+oWqT9uzR3mc9+ArRr344K5i7SPjfm0rZt26VbVoe2TKr8ZtFlVVc0muupU0r8469/4eV/P8Ra7dvw1azv6DpgIOdd/PcG76pdbSEpSZIakGdApTwpKSlh8Hbb89QXY+e3VafEnZ+OYe9D/y9n67npuhuY9vKHHN1va3bvtT7H9NuGKS8O55Ybb8rZOhZnp112ZnjlWGZUzJ7f9vWMb5jappL11ltvmZe7y2678GWMYtbc75c7ceYkKtrPZeDAgUu1rAMOOYAPK0ZQWV05v+3LqV+y0tor1Xsgoob22H/+w6gnHuGijdbiZ2v24+wN1mSVMZ/xj7/8pcHXnZbjnyRJUl0sQKU8Ovk3v+PDHv34w1sfc/WILzjt9Y/Y4ICfsO2QITlbxyN33892KwxYoG37FQbw8B335mwdi9O5c2fOvvQC7v72VR75+nUe+PpV/ssnXHLNP5fr7Gvnzp0579LzeXbWC7w0+RWen/wi77cawT+uW/rlrr322hz7m2N5eupTDP12KM9Pfo6pK33Lny/98zLHl2sP3XozB6++4gJdjndbpS8vP/V4A685kaha5p/6iIhdIuKjiBgZEYuM5BQRh0TEu9mflyNi/Wz7ihHxbESMiIgPIuKXNeb5S0R8mJ3ngYjolKs9IkmScssuuFIetW7dmr9e+S/Gjh3L+PHjWX311WnfPnfdbwEqK+ZSvtBtXVqUljG3YtEurA1h080246FnH2fEiBG0bNmS/v3752Rgn8232JxHX3iM4cOH06pVq+Va7rrrr0uflVbgsw8/pbxVC3bbchc6dOhQ7/kfefgRrvr7v5g1bRbtO7fjl2f8ku132H6ZYqnN7FmzaNt9wduvlERAdXF3j42IUuByYEdgNPB6RDycUhpeY7LPgW1TSlMiYlfgamBToBI4JaX0VkS0B96MiKey8z4FnJlSqoyIi4AzgdPzuGmSJKmeLEClAujduze9e/dukGVvss0WvPPyl2zQY5X5bcMmfMHm223dIOurTVlZGeuuu26DLHd5uvJCZnCmEw/7Odu32YjtV12PyupKXr79BaZNm84Zvz+zzvmfeOwJ/nHW5WzaZUtadWnFrLmz+NMpF9LmqjZsttlmyxXbPNvu9kOeeuwB9lxtpflt742fxKrrLF1346WVgOqG7Uq7CTAypfQZQETcCewFzC9AU0ov15j+VaBvtn0cMC77eHpEjAD6AMNTSk8uNM9+DbkRkiRp2dkFVzlTUVHB3Ln5Ocu2sOrqambPnk1Kze86tKqqKubMmTP/+fGnnMSbLb7hsTHv8v7EL3lszLu83fpbjvvViTlfd0qJ2bNnU11EZ+buvv0u1omV6dG2KwBlJWVs3WNDnv33U8yaNavO+f912b/YuPPmtCptRXV1NW3K2rBh+4254pIrcxbjjw/7KW+36sS1Iz5j6OivufuTL7hxwnROPuvsnK2jdpkSdNl/6tQHGFXj+ehs2+IcATy2cGNErAxsALxWyzw/q20eSZLUODT7M6Dvv/8+991/O5MmTWDw4C3Y70cHLvWoms3duHHj+PNZZzJu5AiqE6y23kb8+o/n07lz5wZfd3V1NTffcj2PPvoAbduWU1VZypFHncg2W2/b4OsutIqKCi654EJeeuIpygnardCD0887hwEDBnDbw/fxzNPP8MkHI9hl4AC232F7WrRokdP1P//sc/zjgj9TPeM7KiKxz6EH87NjjqKqqopHHn6YZx56iFatWrH7wQezzbbbNpr7a37+8af0aL3gYEMRQceydkyePLnO0XqnfTudGbNm8Pn4z6iuTpSUltBzhZ6MHzu+3jEMHTqUO66/jelTp7L9D3divwP2W+D/p3Xr1lx9+x3874UXeP+tt+i/6qocv8suS3W/02W1nKPgdouIN2o8vzqldHWN57W9CWo9ahQR25EpQLdaqL0dcB9wUkpp2kKv/ZZMV93bliF2SZKUB826AH3yqce5555/cdwJO7LCCuvy7H/f44QTjuDyy69frltGNCdz587l5CMP5ef9Slh3+36klHj1qw859dgjuPbO+xq86Ljl1hv5evzr3HzrsZSXlzFlynROP+0SOnfq0iBdQBuTc878De3eHclZa25CSQTjZkzjN0f/nGseuJfu3buz6267sutuuzbIut977z0uPf1sDu83mPbdWzO3qoqHbnmIGyJ4/+036fDZSA7s05s5s2dwzx/OYvg++/Lzk05qkFiW1gabDebltx6nZ9vvr7GsrK5kappJz54965z/u7nf8elXn9KjRU/KS8qYWz2XEZ9/QItN61fg33bTrdzzj1vYrMNAVinvy/P/+DdP/vsxrr3tBsrKvv9ILi0tZch22zFku+2WfiMLZ1JKafASXh8NrFjjeV9g7MITRcR6wLXArimlb2q0l5MpPm9LKd2/0DyHAbsDO6Tm2BWiCdtk0ABG1+NeuDNnzAA8gCxJjV2zLUCrq6u5/vrLufran9G2bebefwcetDUVFZX8+5GHOPCAgwscYXH43//+x6CWs1m3V6YXXUSweb9uvDB0DO+9995yX6+3JCkl/vOf+7jplmMoL8+8lTt3bs8vf7ULd915M+uu2/C3rCiUKVOm8PGrb3DGmt9/1+/VrgND2nbjwXvu5ajjft6g67/5ymvYo/tatG+ROSNXXlrKXiuuz7lXXcOGXdrykwFrz5/2xHU78Lt77+XA//u/RnGbk31+tA/33Xo370z8kAFdVmfqnOm8OGUYR5xyNOXl5XXO/9WXX/H1d+NpVdqKLmVdmVI5hQ+/G07Lj+ouQGfPns3NV1zPgb1+QFlJZqCoLVcYxH+/eJ3nnn2WH+y443Jv3/JKDXsf0NeB/hGxCjAGOAj4cc0JImIl4H7g0JTSxzXaA7gOGJFSuniheXYhM+jQtimluvtRq6iMHjOGsefWXVi2P2landMU0uyZM+jdveMSp+nbpw9Dhw1f4jSSVOya7TWgEyZMoFevDvOLz3m22npthg0bWqCois/4cWNZsfWiZzlXbA1ff/11g667urqaFi1KaNFiwaJh5ZV78vXXi5xUaVK++eYburdstUh7r9btGPvFlw2+/nGjR9OrXacF2spLS5k9bRqD2i34RbEkgnXbt2X48Mbxpapt27bccPfN9Nt3XR6rHMrHK0zk1L//lgN+fOAi086cOZOHH36Y2269jS+/zO7XCtiy61Z8OfdTnp/+NOOqRjGk23bMmjqzznV/9tln9CztPL/4nGeVVr0Y+mJtlzPmVyKRUvUy/9S5/JQqgROAJ4ARwN0ppQ8i4tiIODY72VlAV+CKiBhWo0vvlsChwPbZ9mERsVv2tX8C7YGnsu1X5W6vSLlRWV3N2HPbLvGnPmd6JanYNdszoB06dGD8+KmklBboJvrllxPo2XNJY2KopvU32JCrb62iZkfPlBJvTK5in4ENO2JnaWkp5eVtmTDhW3r06DS//cUXh7Puuhs26LoLbaWVVmJ0xWzmVlUtcMuV96Z9wyZb7tvg699g8015/+l3GbzCqvPbpnw3k7adO/F1ReUi04+rqKBHjx4NHld9derUiZNOO5mTTjt5sdO8++67nHz0yfSY25MW1eXcdPFN7Pbj3WjVtiUtoyVbdPn+0sQpFd/SuWvd1zx3796dqVUzFmmfXDGNDVZqHO/ZBj4DSkrpUeDRhdquqvH4SODIWuZ7kdqvISWltHqOw5QkSQ2k2Z4BbdOmDeuvvxk33/Ts/BE8J0z4lhuue5F99zmgwNEVjwEDBtB6jQ256s1RjJs2m9HfzuJvQ0exzna7NdhtRmr6+c9P5oxf38Gbb37Ct9/O4NFHX+eWm4ZyyI8Pa/B1F1KLFi049Pif889P3uLzb7/h2+9m8/iXH/NVx1bssmvDXPdZ02FHHcGL1RN57etPmVHxHR9NHsdNo9/k3Iv/zOtzKvh0yhQgczDitbHjqOyxAmussUaDx5Ur1dXVnP6L09mi1ZZs0G0D1ukxkB26/YAnbn+C/Q7bj1emv8zsytlUp8SMihkMnfkyvzn/N3Uut0ePHvRbvz/DJn00f8TmSbOm8HGMYa99927graqPRKJ6mX8kSZLq0mzPgAKc/KvT+efll/CTH19O+/atqKos51e/OpuVVlqp7plzZO7cuYwaNYouXbrQqVOnvK13nvHjxzNnzhxWXHHFZR4w6LyLL+ORhx/kqofuo7SsjF1OOJ6ddt4lx5HWbpONN6HTb/7KXXffwtixbzFw4AZc/s8bGsW1hg1tv4MPot/qq3HXdTcwdcpktv7x3px08EE5H+22Nt26deP6++7k1utv4L5XX2fFdfrx56N/zRprrEG/G2/kT2ecwZRh71KZEv033JC/nnd+g8eUS59++iktZrekfef289tKooRVy1alQ5sOHHnWz7jy4iupmlZNebsyfv3X0zjwwEW78Nbmwr//mT+dfR63PfcE5VFGxxU68/d/Xp6XUaMlSZIKrVkXoOXl5fzqpF9TWXkyFRUVtG7dOq+3injkkQe5+aZ/ssoq7fn66xms1G99fvvb82jVatFr+3Jt/Pjx/OGUE+CbUbQuL2F8VWtO/9PFrL/++ku9rNLSUvba50fstc+PGiDSuq2xxhr8/nfnFmTdhbbxxhuz8cYbF2TdXbp04cRTT1mkfaWVVuKq229n9uzZlJaW5qUgzrWysrJab0dSlaopKy+j4rsK+qzQi/Yl7ZmeZvDd7O8W6c6/OG3atOG8P/+JuXPnMnfu3MY14naC6npcyympYdRnoCJwsCJJxa1ZF6DzlJWVLXD7g3x49913efihK7jiH9vTqlVmEJ2HHh7OJRf/iTN/c06DrjulxOnHHcExK81k4HorADBxxhzOOPkYrn3gSTp06NCg61fzkI97VjaUlVdembIupXwz8xu6tukKQFV1FZ9WjmTVtApv3jeUPXrsRkRQnap58obH6LtSX/bYa496r6O8vLxeo+7mnwWoVCjzBiqqS+/fO1iRpOLVbK8BLbT77ruFnx02cH7xCbDnHmsz7J0XqaioaNB1jxgxgh6V3zCw1/dHWbu3a8nOvYInH3+sQdctFYOI4JKrLuHdsnd4bcqrvPXNWzw15UmOOPUIXnjyeQZ323D+2c6SKGHjLoO55ZpbChz18mvoUXAlSZI8A1ogU6dOpmvXXgu0RQTt2pbz3XffNWi3xalTp9Kt5aJdBbu1CiZ+M4lnn32G22/+BzOmTaFXn1U5+rjTWWuttRosnqaooqKCG6+5gmcfvR+qqxi89Y4cdcLJnl1u5ObMmcO//nkVTz78GCkltt1pOzbbenOqqqrYaKON6NixI9dffh3lrRY8c9m6vBUzpk8vUNSSJEnFwzOgBbLZZtvz32c/X6Dt66+nUVXdtsGLlHXXXZc3J1VRUfn9GYuUEs+Oq6K6JLj/1rM465h23HjhGvxs95mc87sj+Oyzzxo0pqbmD78+kZK3buaqXcq5do/WrDbuEU466pD5Iy6rcfrVz3/Jx/e+zr4dtma/jtsy/tEPueayqxgyZAgdO2Z6DAxYfwCjpy3Y/e2zyZ+x6dabFSLknKtejn+SJEl1sQAtkL33/hGvv1XFNde9wYgRX/Pk0x/xu7Nf5le/+kODr7tdu3YceOyv+PWzY3n1i294Z8y3/OmlcXTfYDte+O+DnHXC6vTomrl+r/8qHTnhx9259aYrGjyupuLLL79k2mdv8JONu9KyvITSkmCXdTqzRtlYXnzxxUKHp8X4+OOPmTB8NJv0WJeykjJKS0oZ1H1tGPsdb7zxxvzpTvvdr3m/ZATvTXqfr6eP551J7/JZm1Ecf9LxBYw+l9Jy/EiSJC2ZXXALpFWrVlxxxc08+eTjPP70K/Tovj6XXno+PXv2zMv69z3gINZZfwMevf8uZs+ayV5n7s2mm27KTw7Ymg7tFuz+u+6aXbjmgQ/zElcufffdd/zn0Ud477036dmzD/vsvR8rrLBCg6/3888/Z52ui34ZX6dr4vORH7HNNts0eAxa1KxZs/j3gw/xzquvseJqq/Kjgw+iR48e81///PPP6Z4W7X3QrboDn336GZtssgkAffr04a5H7ub+e+7n4+EfscMGO7PXPnvRrl27esfyySefcN/tdzFt8lS2330ntt9hB0pKGsPxwESiqtBBSJKkJswCtIBatGjB7rvvye6771mQ9a+55pqseeZZC7SVlrVj2oyKBYrQd0Z8w6qrDch3eMtl1qxZHH/CEWy5VR8OPHhNvvhiAiefciRnnPEn1lt3vQZd96qrrso93yx6je17k0rYur/X0hbCtGnTOPqgH7NuRQmbdurGqI9f4Jh77uOi669hjTXWADL/bxNj6iLzToyprN5/9QXaOnbsyOFHHr5Msfzn349w9TkXs2X7VelT3pq7h/6Tf9/zAJdc9c9GUYSm5JlMqbHzdi2SipkFqBZw6OEn8cfLzuW0o1Zhhe5tGDFyCpffMYkL/nZpoUNbKvfceyc77rQKBx60NQBrrNGXddZZiXPOvohrr7mtQde90kor0bn/Ztz42ssctGEnykqCx4d/y6fVfThjyy0bdN2q3a3X38BmVS0Y0m8VAPp17MzK0zrytz/8kX/dkXk/9O/fn17r9uOV995hcLd1iAje/eZjylZqx0YbbZSTOCoqKrjigov52Upb0bK0PBtLDx54/3VeeeUVtvT9IakevF2LpGJW+MPtTVR1dTWvv/46TzzxBBMmTCh0OPW20867csDhf+LCGyr46W9GcvvTXTjyuHP48MMP+eijjwodXr0NHfoiO+40aIG2Pn26MXfuDObMmdPg6z/7or/TcoujOOEpOOo/FYzpty+XXnt7ozjD1Ry9/Mx/2XSFvgu09e3QifFffLXAGb+/Xf53BhywGdeNeZirv3yAFXZdkytvvHr+LVeW18iRI+lV2mF+8TnPOm168b+nns3JOpZHIpGoXuYfSZKkungGtAGMHTuW0884kbXX7kLPnu25884r2GyznTjmmOIYpGSbbYawzTZDmDt3Lr/7/a/5z6PXsf6gvjz73NfMmdOWCy+4hFatWhU6zCXq1KkL48dPoUuX9vPbqqurmT17LuXl5UuYMzfKyso4/Kifc/hRP2/wdalunbt2ZcqsWbRu/32XtbnVVaSy0gWKy2Fvv83TD9zPkC7dKSV45fHH2WTzjdn+Bz/ISRydOnVietV3i7R/WzGLFXt2z8k6lpf385QkSQ3J0zEN4Nxzf8spp27Hr0/fi8N+uj3/uuZIPv7kRV5//fVCh7ZUbrn1RvqvUcpf/nYoPzl0O/504cFstnkXrrn2ykKHVqcf7ftjrvjnU3z3XQWQua7t9tteYPPNh3gWshk66Mifcffokcytygywk1Lika9GsvO+e8+f5rvvvuO8U37NL1cayI/6rc3e/dbi1FUHcelZ5/Dtt9/mJI7evXvTYZUV+GDSV/PbplfM4s05o9nrR/vkZB3LJ0GqXvYfSZKkOvhNPAdmzpzJrFmzAJg8eTJVVVMZOHDl+a+XlJTw40O24PHHHy5QhMvmuWcf56CDt1qgbe99NuPllwvfVbAuG264Ibvueig/++k1/OaMOzj8sCsZPaoVxx93UqFDUwFsudVW7P6LYzjv02Fc9fn7nPvRG7TZZhOOOfEX86d57bXXWLdlBzq2bD2/rU15CzZu3Ynnn3suZ7H85fK/81W/4PpRL3D7mFe5a9rb/OZv59KyZctGMQBQWo5/kiRJdbEL7nIYO3YsF1z4B6ZPn0BK0KVLH44+6heUli5a15eVlVJdXVy3N6iurl5kW0pKolF8Sa6PPXbfi1123o1x48bRuXNn2rdvX/dMarL2O/hg9vzRj/j666/p0qXLIrdNqaqqImp5b5cSVFXl7m+3U6dOXH7D1UyePJmpU6fy4IP38I9//omOnVozc2Y1xxz9K7be2lv1SJKkpskCdBlVVlby61+fwKmn/4B1182MrPnmm59w0UVnU1ragk8/Hcdqq/UCMt397rn7NXbZ+ahChrzUttp6Bx568DX2P+D7s6CPP/4WG29cPCN1lpeXs9JKKxU6DDUSLVq0WOz7YbPNNuPvc6ax89wK2pRnbkNUUVXJa7Mmc/SQITmPpUuXLtxyy/W0bjOGm245ipKSEr79dgannfpXevZcYf7tYfIqeQ2oJElqWBagy+jll19mo417zy8+ATbaqD/913ifgev8kLP/cB1bbNmPFXp24LlnP2bllTcoulss/PSwIzn5lOP5cMS9DNqgLx+8P44vvviOSy6+otChSTnXpk0bfnXu2fz1t39gcJvOlBG8NmsyPzv9FLp27Zrz9c2dO5eXX36Gm289ev51yZ06teOYY7bhnntu5be/PSfn66xbcjRbSZLUoCxAl9GEiRPo23fRm0D3XbET5eXl3HTjvfzvf//jm28mccopR7DqqqsWIMrl06pVKy7/57W8/fbbfDLyY3b8wQ8ZPHiwg/gsxpw5c7j91ut54akHKC0rZ9e9D2WffQ9o0P319NNPcv89NzBt+lQ23WwI/3fYMXTsWPfNyZfG0NeHcscdNzBp4njWGbgBh/3fkfTq1Sun62gsttt+ezZ8fCOee+45qiorOXrIkAYpPgFmz55Nx05tFnl/rLhSdyZO/LhB1lmXBBagkiSpQVmALqNB6w/isn/cwz77bjn/Ng4pJV7830j+ePZJtGjRgh122KHAUS6/iGDDDTdkww03LHQojVpKiVNPPJyNug7nssM6UlE5mxufuoALPnib3/7hogZZ5003Xs0Hb9/JacesQdfOPXnqhdc4/ucvcM1199C6deu6F1APTz/zJPfd9y9OPvWHrLxyT156aTgnn3IMl/79Wnr06JGTdTQ2HTt2ZK+99mrw9bRv355Zs6qZMmUGnTt/fz3qCy8MZ4MNNmvw9UuSJBWCp7KW0eqrr07Pnmtx4QX389ln4xg5cix/PPtuBgzYjN69exc6POXZG2+8Qbf0EYfu2IU2rUrp1K6cX+7dhVEfPM2YMWNyvr7Zs2fz6CO3ctbJ67NCj7aUl5ey2w4rs/XGwaOP/jsn60gpccP1V/CnC3/Mqqv2oqSkhK23Hshhh2/GnXfdmpN1NGcRwc+PPYXTTr2T14d+zLhxk7n77pd57NFP+dGPDihgZNXL8SNJkrRkngFdDmee8QeefuZprr36P5SUlLDTTocxZNsheVv/zJkzefyJxxgz5ksGDFifIdsOoazM/9KGVlVVxQv/e4H333+bnj37sOsuuzHig3cYvFrlAtNFBBuukvj444/p06dPTmMYPXo0q6/cdpFRijdatwtPvPIW5KCA+e6772jZKujQoc0C7Rtv3J+HH3x0uZe/LIYPH85zzz9Ny5at2GXnH+Z8v+bblltuRY8ePbn33tuYMOFTBg3ahCuv+P0iI/TmT4IiGeVaUu5sMmgAo+txsLRvnz4MHTY8DxFJasqsVpZDSUkJO+24EzvtuFPe1z1q1Ch+ffrx7LLr2mw4uCdDX3uAu+66mUv/fhVt2rSpewFaJnPmzOFXJx/Hyqu0YIst+/PF529z1NG3svsPD+TjcaXsstD0n3xdwnZ9++Y8jp49e/Ll6JmklOZ3AQf45PNp9F1x65yso2XLlsycOZeKirm0aFE+v/3jj8fQt2+/nKxjaVx+xaV89NHL7LXPhsya9S1n/uZYDjnkOHbeade8x5JL/fv358wzzy50GPN5DajU/IweM4ax57atc7rev899jx5JzY8FaJH6+6UXcfqZu8wfhXfzzdfmzjte4M67budnhx9Z4Oiarvvuv4eNBnfmp4dnru/dfPO12Wjwavzj7/9lxsRuvPj+ZLZcpz0pwb9fmUpFmwH0798/53F06NCB9TbYjuvueIP/268/LVqUMuLjb3joyalcdc1+OVlHSUkJe+55ABdd+CCnnLoHbdq04quvJnD5P57hnD9empN11NfIkSMZPvxFLv3H4fML7u22W48jDv8H22w9JGfXvAoyQxFJUnHxLK5UPCxAi1BKidGjP2PddX+4QPsee27CSSfeZQHagF566b+cdfaCZ7zXWKMv06ZP5C+X3cSVl/6Jyx9/BaKUjbf8IRf87cw6lzlhwgS++OILVl555aUa2OeUU8/i+uuu5Ohf3w+pkj4rrcGFf7meTp06Le1mLdaPDz6Ue+9rwbFH30R1dSWdOnXjtNPOy/uozv978Xl22329Bc72tm7dkk03W5Vhw4ax+eab5zWeJZk1axbDhw+nS5cuRTn6tSQVI8/iSsXDArQIRQTV1Ynq6uoFbuEwdepM2rYt1LVjzUObNm2ZNm0W3bp9f6uTlBIVFZX07NmTcy78R72XVVVVxYUXncPnn7/HgHV6c/U1Y1lllXU54/SzKC0trXP+0tJSjjr6BI46+oRl2pb6iAj23+8g9t/voAZbR320adOW6dO+W6R9+rTvaNu27i8c+XLf3bdx700Xs2G/KsZPDWaUr8KfLr6WLl26FDq0ekqQ7IIradnV90zk3IrvKG/Rqs7pmtMZS8/iqrmwAC1Sm28+hHvveYkDDsxc71ddXc2/rnqK3Xc/uMCRNW177LE/1/zrX5x/wY/nF/+P/Hso66+/yVLf7/O222+mY6cp/Ouao+a3XXnFY9x+xy0c+pOf5jLsorfTjjtz3PG3sONOg+jUKXOQZeTIsYz8ZAoDBw4scHQZH3zwAU/fdRHXH9WWFuWZ98KrH37Kub89kUuuLJ5Rg5NdcKUmY/bMGfTuXve9oWfOmAHUfTCvPsubOWMGUy/pWeey2p80jYkX1X2f5c4nf5TTbWjMPIur5qJoCtCI2AW4FCgFrk0pXVjgkArq+ONO4o/n/Jbnn7uOVVfrwbvvjGLrrXdhpx13LnRoTdo2W2/DZ599zGGHXsn6g1biyy8m0bbtCvzx7KV/Oz791CNcftVPFmg77Kfbcfyxt1qALqRz58788pe/47hjz2fddXsza9YcxoyexXnnXbzUhX9DeeT+WzhsK+YXnwCbrdWOG14czpQpU+jcuXMBo1saDXsGtK7P8sj0s74U2A2YBfw0pfTWkuaNiC7AXcDKwBfAASmlKQ26IU2EubVpq6yurldB0/6kaTlbXn2XVV+53gZJhVcUBWhElAKXAzsCo4HXI+LhlFKz7X/QsmVL/nT+X/n6668ZN24cxx69Gh06dCh0WM3CTw87kh/teyAjR46kZ8+ey3zf17mVc2nVqsUCba1atWBu5dxchNnkbLbp5tx264MMHz6cVq1a0b9//wWuCS20WTOm0a77ol2n27TMjJ6sen+W7wr0z/5sClwJbFrHvGcAz6SULoyIM7LPT8/XdhUrc6uao/qeFa5PF+H6doWtb9fapnAWV6qPoihAgU2AkSmlzwAi4k5gL6DZJ8kVVliBFVZYodBhNDvt27dngw02WK5lbLDBpjzzzDvsuOP3y3n66WFssMGmyxtek1VWVsZ6661X6DBqtc1O+/LvO//H2it+3zZucgXfVnamZ8+6u6M1Gg17H9D6fJbvBdycUkrAqxHRKSJ6kTm7ubh59wKGZOe/CXgOC9D6MLeq2VmaM6p1dRGub1fY+nat9SyumotiKUD7AKNqPB9N5si4VLSOPup4TjzxKEZ+Mp5111uR994dxdBXx3LZZdcUOjQtg+22255nHtuas+9+ie3WrmL8t4mH3mvD7y66tFGdqV2y1NDXgNbns7y2afrUMW/PlNI4gJTSuIio/3DSzZu5VSpChTiLW18OQrUoB5daVKSGPdqdExGxP7BzSunI7PNDgU1SSr9YaLqjgaOzTwcC7+c10MajGzCp0EEUQHPdbnDbm+u2r5lSap+rhUXE42T257JqBdQcrvjqlNLVNZZf52d5RPwHuCCl9GL2+TPAr4FVFzdvRHybUupUYxlTUkrFctFtwZhb8645f1YtD/fbsnPfLRv327Kr1/eSYjkDOhqo0bGNvsDYhSfKftG5GiAi3kgpDc5PeI1Lc9325rrd4LY3523P5fJSSrvkcnm1qM9n+eKmabGEecdHRK/s2c9ewIScRt10mVvzyH23bNxvy859t2zcb8uuvt9LGsfwkXV7HegfEatERAvgIODhAsckSVo69fksfxj4v8jYDJia7V67pHkfBg7LPj4MeKihN6SJMLdKkvKuKM6AppQqI+IE4AkyQ8Vfn1L6oMBhSZKWwuI+yyPi2OzrVwGPkrkFy0gyt2E5fEnzZhd9IXB3RBwBfAXsn8fNKlrmVklSIRRFAQqQUnqUzBeT+rq67kmarOa67c11u8Ftb66Kbttr+yzPFp7zHifg+PrOm23/Btght5E2D+bWvHLfLRv327Jz3y0b99uyq9e+K4pBiCRJkiRJxa9YrgGVJEmSJBW5JleARsQuEfFRRIyMiDMKHU+uRcSKEfFsRIyIiA8i4pfZ9i4R8VREfJL93bnGPGdm98dHEbFz4aJffhFRGhFvR8Qj2efNZbs7RcS9EfFh9v9+82a07b/Kvtffj4g7IqJVU932iLg+IiZExPs12pZ6WyNio4h4L/vaZRFFcyNSNVJNPbc2lNr+plU/i/u+oyXL5sihEfFOdr/9sdAxFZOFv2eqfiLii+z3jmH1GQm3SRWgEVEKXA7sCgwADo6IAYWNKucqgVNSSmsDmwHHZ7fxDOCZlFJ/4Jnsc7KvHQSsA+wCXJHdT8Xql8CIGs+by3ZfCjyeUloLWJ/MPmjy2x4RfYATgcEppYFkBko5iKa77TeSibumZdnWK8nct7F/9qehb6+iJqyZ5NaGciP+/S2rxX3f0ZLNAbZPKa0PDAJ2icyI4qqfhb9nqv62SykNqs8tbJpUAQpsAoxMKX2WUqoA7gT2KnBMOZVSGpdSeiv7eDqZP5I+ZLbzpuxkNwF7Zx/vBdyZUpqTUvqczMiSm+Q16ByJiL7AD4FrazQ3h+3uAGwDXAeQUqpIKX1LM9j2rDKgdUSUAW3I3KewSW57SukFYPJCzUu1rZG5D2aHlNIr2QF9bq4xj7QsmnxubSiL+ZtWPSzh+46WIGXMyD4tz/444Es9LOZ7phpAUytA+wCjajwfTRP+sIqIlYENgNeAntl75ZH93SM7WVPaJ38Hfg1U12hrDtu9KjARuCHbLeTaiGhLM9j2lNIY4K9kbq0xjsw9IZ+kGWx7DUu7rX2yjxdul5ZVU/y7UhFZ6PuO6pDtRjoMmAA8lVJyv9XP31n0e6bqJwFPRsSbEXF0XRM3tQK0tuucmuRRn4hoB9wHnJRSmrakSWtpK7p9EhG7AxNSSm/Wd5Za2opuu7PKgA2BK1NKGwAzyXbDXIwms+3Z6x33AlYBegNtI+InS5qllrai3PZ6WNy2Nqd9oPzwPaWCWYrvO8pKKVWllAYBfcn0jBlY4JAavWX4nqkFbZlS2pDMpRrHR8Q2S5q4qRWgo4EVazzvS6a7XpMSEeVkPoxvSyndn20en+16R/b3hGx7U9knWwJ7RsQXZLp/bR8Rt9L0txsy2zK6xhHMe8kUpM1h238AfJ5SmphSmgvcD2xB89j2eZZ2W0dnHy/cLi2rpvh3pSKwmO87qqfs5TrP4XXI9bG475mqh5TS2OzvCcAD1HH5U1MrQF8H+kfEKhHRgswAHQ8XOKacyo5meR0wIqV0cY2XHgYOyz4+DHioRvtBEdEyIlYhMyDJ0HzFmysppTNTSn1TSiuT+X/9b0rpJzTx7QZIKX0NjIqINbNNOwDDaQbbTqbr7WYR0Sb73t+BzHVAzWHb51mqbc12050eEZtl99n/1ZhHWhZNPreq8VnC9x0tQUR0j4hO2cetyRzI/bCgQRWBJXzPVB0iom1EtJ/3GNgJWOLI32X5CCxfUkqVEXEC8ASZ0TKvTyl9UOCwcm1L4FDgvWz/foDfABcCd0fEEWS+tO8PkFL6ICLuJlOwVALHp5Sq8h51w2ku2/0L4Lbsl7/PgMPJHEBq0tueUnotIu4F3iKzLW8DVwPtaILbHhF3AEOAbhExGvgDy/Ye/zmZ0TdbA49lf6Rl0kxya4Oo7W86pXRdYaMqGrV+30kpPVq4kIpCL+Cm7OjVJcDdKSVvKaKG1BN4IHPMiDLg9pTS40uaITKDJEqSJEmS1LCaWhdcSZIkSVIjZQEqSZIkScoLC1BJkiRJUl5YgEqSJEmS8sICVJIkSZKUFxagkiRJahYi4pKIOKnG8yci4toaz/8WEScvZt5zIuIHdSz/7Ig4tZb2ThFx3HKELjUZFqDSQiJixYj4PCK6ZJ93zj7vV8u0rSPi+ez9tuq7/BMi4vBcxixJkurlZWALgIgoAboB69R4fQvgpdpmTCmdlVJ6ehnX2wmwAJWwAJUWkVIaBVwJXJhtuhC4OqX0ZS2T/wy4P6VUtRSruB44cfmilCRJy+AlsgUomcLzfWB69mBzS2BtgOzB5TezZ0h7ZdtujIj9so93i4gPI+LFiLgsIh6psY4BEfFcRHwWEfPy/YXAahExLCL+EhG9IuKF7PP3I2LrfGy81BiUFToAqZG6BHgz201nK+AXi5nuEODHABExBPgjMB4YBNwPvAf8EmgN7J1S+jSlNCsivoiITVJKQxtwGyRJUg0ppbERURkRK5EpRF8B+gCbA1OBEWS+A+yVUpoYEQcC55M54AxARLQC/gVsk1L6PCLuWGg1awHbAe2BjyLiSuAMYGBKaVB2GacAT6SUzs/2omrTYBstNTIWoFItUkpzI+I04HFgp5RSxcLTREQLYNWU0hc1mtcnc/R0MvAZcG1KaZOI+CWZIvak7HRvAFsDFqCSJOXXvLOgWwAXkylAtyBTgI4BdgKeigiAUmDcQvOvBXyWUvo8+/wO4Ogar/8npTQHmBMRE4CetcTwOnB9RJQDD6aUhuVgu6SiYBdcafF2JZN0Bi7m9W7Atwu1vZ5SGpdNPJ8CT2bb3wNWrjHdBKB3ziKVJEn1Ne860HXJdMF9lcwZ0C2A54EPUkqDsj/rppR2Wmj+qGP5c2o8rqKWEz4ppReAbcgUvLdExP8t05ZIRcgCVKpFRAwCdgQ2A3417/qPhcwGWi3UVjPpVNd4Xs2CCahVdn5JkpRfLwG7A5NTSlUppclkBgnaHLgL6B4RmwNERHlErLPQ/B8Cq0bEytnnB9ZjndPJdMklu9x+wISU0jXAdcCGy745UnGxAJUWEpk+N1cCJ6WUvgL+Avx14elSSlOA0uy1IEtrDTJHXSVJUn69R6YX06sLtU1NKU0A9gMuioh3gGF8P2gRACml2WRGtH08Il4kM/bD1CWtMKX0DfBSdsChvwBDgGER8TbwI+DSHGyXVBS8BlRa1FHAVymlp7LPrwB+GhHbppSeX2jaJ8kMUrS0w7JvSWbAIkmSlEfZkes7LNT20xqPh5HpHrvwfD+t8fTZlNJa2YPWl5MZ24GU0tkLzTOwxuMfL7TIm5YlfqnYRUqp0DFIRSsiNgBOTikd2pDzSJKkxiMifgUcBrQA3gaOSinNKmxUUnGwAJWWU0T8DLipvvcCjYgdgU8WGj1XkiRJavIsQCVJkiRJeeEgRJIkSZKkvLAAlSRJkiTlhQWoJEmSJCkvLEAlSZIkSXlhASpJkiRJygsLUEmSJElSXliASpIkSZLywgJUkiRJkpQXFqCSJEmSpLywAJUkSZIk5YUFqCRJkiQpLyxAJUmSJEl5YQEqSZIkScoLC1BJkiRJUl5YgEqSJEmS8sICVJIkSZKUFxagkiRJkqS8sACVCigifhMR1xY6jlyLiEMi4slCxyFJKn4R8VxEHFnoOJZWRHwRET8odBy1iYitI+KjQseh5skCVFoO2eQyPiLa1mg7MiKeq8/8KaU/pZRynlSzyfq7iJgREZMi4v6I6JXr9SxOSum2lNJONeJJEbF6vtYvScqvbD6cHRHTI+LbiHg5Io6NiEbxXTMifhoRLxY6jnki4saIqMjm6ckR8VRErJWv9aeU/pdSWrNGPI22WFbT0yg+FKQiVwb8stBB1OKElFI7YA2gE3DJ0swcGX5GSJLqa4+UUnugH3AhcDpwXWFDyo2IKGuAxf45m6f7AhOAG5d2ARFRmuugpIbml0tp+f0FODUiOtX2YkRcGhGjImJaRLwZEVvXeO3siLg1+/jxiDhhoXnfiYh9s4/Xyh4hnRwRH0XEAfUJLqU0GbgPGJhdzhYR8XpETM3+3qLG+p6LiPMj4iVgFrBqHdP/NCI+yx7x/jwiDqnR/mL28QvZyd/JHuk9MCLej4g9aiynPHumdlB9tkmS1HillKamlB4GDgQOi4h5+adlRPw1Ir7K9h66KiJaz5svIvaKiGHZfPlpROyy8LJr5s3s85WzvWzKss8XyUsRsTZwFbB5Ng99W1c8ETEkIkZHxOkR8TVwQ0SURMQZ2di+iYi7I6JLjVgOjYgvs6/9din21yzgdr7P02tn8/G3EfFBROxZYx03RsSVEfFoRMwEtqtj+t0iYnh2f4yJiFNrbl/28S3ASsC/s/vn1xHxn4j4xUL7/t2I2Lu+2yUtjgWotPzeAJ4DTl3M668Dg4AuZBLMPRHRqpbpbgcOnvckIgaQOYr8n8h08X0qO02P7HRXRMQ6dQUXEd2AHwFvZxPlf4DLgK7Axdnld60xy6HA0UB7YPrips/GdBmwa/aI9xbAsIXXn1LaJvtw/ZRSu5TSXcDNwE9qTLYbMC6ltMj8kqTilFIaCowG5h14vYhMr5xBwOpAH+AsgIjYhExuOI1Mr51tgC+WZn2Ly0sppRHAscAr2TzUqa54slYgk7v7kcmLJwJ7A9sCvYEpwOXZdQ8AriSTQ3uTyZl96xl3O+AQMnm6HPg38CSZfP8L4LaIWLPGLD8GzieTp1+rY/rrgGOy+2Mg8N+F159SOhT4iswZ7HYppT8DN1EjT0fE+tn982h9tklaEgtQKTfOAn4REd0XfiGldGtK6ZuUUmVK6W9AS2DNRZYADwCDIqJf9vkhwP0ppTnA7sAXKaUbsst5i8xZzf2WENNl2aO87wDjgJOBHwKfpJRuyS7nDuBDYI8a892YUvogpVQJ7FTH9NXAwIhonVIal1L6oM49lXErsFtEdMg+PxS4pZ7zSpKKx1igS0QEcBTwq5TS5JTSdOBPwEHZ6Y4Ark8pPZVSqk4pjUkpfbgM66tXXqpHPPOW9YeU0pyU0mzgGOC3KaXR2dx8NrBf9uzrfsAjKaUXsq/9Pjv/kpyazdMjgXbAT4HNso8vTClVpJT+CzxCjQPUwEMppZdSStVkiuclTT8XGBARHVJKU7LfH+rjIaB/RPTPPj8UuCulVFHP+aXFsgCVciCl9D6ZD/wzFn4tIk6JiBGR6cL6LdAR6FbLMuadbZyX/A4Cbss+7gdsmu1e8212OYeQOTq7OCemlDqllPqklA5JKU0kc1T2y4Wm+5LMUc15RtV4vNjpU0ozyXSvOhYYl+2uU68BFFJKY4GXgB9FpuvyrjW2VZLUdPQBJgPdgTbAmzXy2OPZdoAVgU+XZ0VLmZfqigdgYkrpuxrP+wEP1Jh+BFAF9CSTL+fnz2ws39QR8l+zeXqFlNKeKaVP5y0nW1zOU1eeXtL0PyLTy+jLiHg+IjavI6Z58c8B7gZ+EpnxIA7GA8XKEQtQKXf+QOZo6vwkEZnrPU8HDgA6Z7v9TAViMcu4Azg4myBaA89m20cBz2cT1byfdimlny9ljGPJJNCaVgLG1Hie6jt9SumJlNKOQC8yZ0avWYpY5nXv2Z9Mt6gxdUwvSSoiEbExmZz4IjAJmA2sUyOPdcwOwgOZPLdaPRY7k0zhOM8CB2KXkJdq5jbqEU9t84wi0723Zi5ulc1f48gU0fO2vQ2ZbrhLayywYiw4CGBdeXqx06eUXk8p7UWme+6DZIrK2iy8rZDJ04cAOwCzUkqvLMV2SItlASrlSEppJHAXmWtE5mkPVAITgbKIOAvoUMvs8zxKpuA7h0xXl3lHNB8B1sgOcFCe/dk4O7DC0ng0u5wfR0RZRBwIDMguf6mmj4ieEbFn9pqbOcAMMkeCazMeWHWhtgeBDcmMIHzzUm6HJKmRiogOEbE7cCdwa0rpvWw+uwa4JCJ6ZKfrExE7Z2e7Djg8InbIDvbTZzFnL4cB20TEShHRETizxnqXlJfGA30jogVAPeKpzVXA+fMulYmI7hGxV/a1e4HdI2Kr7DrOYdm+Z79Gpsj+dTbXDyFz2cudSzt9RLSIzCBMHVNKc4FpLEWezhac1cDf8OyncsgCVMqtc4C2NZ4/ATwGfEymS8x3LNh1ZgHZLi/3Az8gM+DQvPbpZK7HPIjM0c6vyQye0HJpgkspfUPmetJTyHQN+jWwe0pp0jJMX5JtH0ume9W2wHGLWfXZwE3ZbksHZJc9m8x1rKtkt1mSVNz+HRHTyeS535IZuO7wGq+fTuZ6x1cjYhrwNNkxEbIDFh1O5pZhU4HnWbQHDimlp8gc7H0XeJMFD6AuKS/9F/gA+Doi5uW8xcazGJcCDwNPZrfzVWDTbFwfAMeTyd3jyAxQNHoJy6pV9hrLPclcmjIJuAL4v8VdD1uP6Q8Fvshu37EsOABgTRcAv8vm6ZqDKt4MrEtm7AYpJyKl2s64S1LDy54RXiOltLiEKEmSCiQi/g84OqW0VaFjUdPREDfVlaQ6ZW8JcwSZo7OSJKkRyV7HehyZs6pSzuS9C25ErBgRz2ZHBf0gIn6ZbT87MjfIHZb92a3GPGdGxMiI+KiOvvmSikBEHEWmi9ZjKaUXCh2P1BSYXyXlSvbzYCKZa0Nvr2NyaankvQtuRPQCeqWU3oqI9mT67+9NZpTQGSmlvy40/QAyI4NuQmao6afJdNlb3EXUkiQ1O+ZXSVIxyPsZ0OxNgd/KPp5O5h5KfZYwy17AndmbAH9O5mLxTRo+UkmSiof5VZJUDAo6Cm5ErAxsQGYIaYATIuLdiLg+Ijpn2/qw4Kiho1lyQpUkqVkzv0qSGquCDUIUEe3I3ILhpJTStIi4EjiXzI1wzyVzz6GfAVHL7LX2G46Io4GjAdq2bbvRWmvVdvsoSVJz9uabb05KKXUvdBwNJdf51dwqSaqP+ubXghSgEVFOJjnellK6HyClNL7G69fw/X2dRgMr1pi9L5n7Oy0ipXQ1cDXA4MGD0xtvvJH74CVJRS0ivix0DA2lIfKruVWSVB/1za+FGAU3gOuAESmli2u096ox2T7A+9nHDwMHRUTLiFgF6A8MzVe8kiQVA/OrJKkYFOIM6JZk7vv3XkQMy7b9Bjg4IgaR6f7zBXAMQErpg4i4GxgOVALHO0KfJEmLML9Kkhq9vBegKaUXqf26k0eXMM/5wPkNFpQkSUXO/CpJKgYFHQVXkiRJktR8WIBKkiRJkvLCAlSSJEmSlBcWoJIkSZKkvLAAlSRJkiTlhQWoJEmSJCkvLEAlSZIkSXlhASpJkiRJygsLUEmSJElSXliASpIkSZLywgJUkiRJkpQXFqCSJEmSpLywAJUkSZIk5YUFqCRJkiQpLyxAJUmSJEl5YQEqSZIkScoLC1BJkiRJUl5YgEqSJEmS8sICVJIkSZKUFxagkiRJkqS8sACVJEmSJOWFBagkSZIkKS8sQCVJkiRJeWEBKkmSJEnKCwtQSZIkSVJeWIBKkiRJkvLCAlSSJEmSlBcWoJIkSZKkvLAAlSRJkiTlhQWoJEmSJCkvLEAlSZIkSXlhASpJkiRJygsLUEmSJElSXliASpIkSZLywgJUkiRJkpQXFqCSJEmSpLywAJUkSZIk5YUFqCRJkiQpLyxAJUmSJEl5YQEqSZIkScoLC1BJkiRJUl5YgEqSJEmS8sICVJIkSZKUFxagkiRJkqS8sACVJEmSJOWFBagkSZIkKS8sQCVJkiRJeWEBKkmSJEnKCwtQSZIkSVJeWIBKkiRJkvLCAlSSJEmSlBcWoJIkSZKkvLAAlSRJkiTlhQWoJEmSJCkvLEAlSZIkSXlhASpJkiRJyou8F6ARsWJEPBsRIyLig4j4Zba9S0Q8FRGfZH93rjHPmRExMiI+ioid8x2zJEmNnflVklQMCnEGtBI4JaW0NrAZcHxEDADOAJ5JKfUHnsk+J/vaQcA6wC7AFRFRWoC4JUlqzMyvkqRGL+8FaEppXErprezj6cAIoA+wF3BTdrKbgL2zj/cC7kwpzUkpfQ6MBDbJa9CSJDVy5ldJUjEo6DWgEbEysAHwGtAzpTQOMkkU6JGdrA8wqsZso7NtkiSpFuZXSVJjVbACNCLaAfcBJ6WUpi1p0lra0mKWeXREvBERb0ycODEXYUqSVFRynV/NrZKkXCpIARoR5WSS420ppfuzzeMjolf29V7AhGz7aGDFGrP3BcbWttyU0tUppcEppcHdu3dvmOAlSWqkGiK/mlslSblUiFFwA7gOGJFSurjGSw8Dh2UfHwY8VKP9oIhoGRGrAP2BofmKV5KkYmB+lSQVg7ICrHNL4FDgvYgYlm37DXAhcHdEHAF8BewPkFL6ICLuBoaTGeHv+JRSVd6jliSpcTO/SpIavbwXoCmlF6n9uhOAHRYzz/nA+Q0WlCRJRc78KkkqBgUdBVeSJEmS1HxYgEqSJEmS8sICVJIkSZKUFxagkiRJkqS8sACVJEmSJOWFBagkSZIkKS8sQCVJkiRJeWEBKkmSJEnKCwtQSZIkSVJeWIBKkiRJkvLCAlSSJEmSlBcWoJIkSZKkvLAAlSRJkiTlhQWoJEmSJCkvLEAlSZIkSXlhASpJkiRJygsLUEmSJElSXliASpIkSZLywgJUkiRJkpQXFqCSJEmSpLywAJUkSZIk5YUFqCRJkiQpLyxAJUmSJEl5YQEqSZIkScoLC1BJkiRJUl5YgEqSJEmS8sICVJIkSZKUFxagkiRJkqS8sACVJEmSJOWFBagkSZIkKS8sQCVJkiRJeWEBKkmSJEnKCwtQSZIkSVJeWIBKkiRJkvLCAlSSJEmSlBcWoJIkSZKkvLAAlSRJkiTlhQWoJEmSJCkvLEAlSZIkSXlhASpJkiRJygsLUEmSJElSXliASpIkSZLywgJUkiRJkpQXFqCSJEmSpLywAJUkSZIk5YUFqCRJkiQpLyxAJUmSJEl5YQEqSZIkScoLC1BJkiRJUl5YgEqSJEmS8sICVJIkSZKUFxagkiRJkqS8sACVJEmSJOWFBagkSZIkKS8sQCVJkiRJeWEBKkmSJEnKCwtQSZIkSVJeFKQAjYjrI2JCRLxfo+3siBgTEcOyP7vVeO3MiBgZER9FxM6FiFmSpMbM3CpJKgaFOgN6I7BLLe2XpJQGZX8eBYiIAcBBwDrZea6IiNK8RSpJUnG4EXOrJKmRK0gBmlJ6AZhcz8n3Au5MKc1JKX0OjAQ2abDgJEkqQuZWSVIxaGzXgJ4QEe9muxF1zrb1AUbVmGZ0tm0REXF0RLwREW9MnDixoWOVJKkYmFslSY1GYypArwRWAwYB44C/ZdujlmlTbQtIKV2dUhqcUhrcvXv3BglSkqQiYm6VJDUqjaYATSmNTylVpZSqgWv4vivQaGDFGpP2BcbmOz5JkoqNuVWS1Ng0mgI0InrVeLoPMG8Uv4eBgyKiZUSsAvQHhuY7PkmSio25VZLU2JQVYqURcQcwBOgWEaOBPwBDImIQmS5AXwDHAKSUPoiIu4HhQCVwfEqpqgBhS5LUaJlbJUnFIFKq9ZKPojd48OD0xhtvFDoMSVIjExFvppQGFzqOYmRulSQtTn3za6PpgitJkiRJatosQCVJkiRJeWEBKkmSJEnKi+UuQCNiYC4CkSRJGeZWSVJTlYszoFdFxNCIOC4iOuVgeZIkNXfmVklSk7TcBWhKaSvgEDI3tH4jIm6PiB2XOzJJkpopc6skqanKyTWgKaVPgN8BpwPbApdFxIcRsW8uli9JUnNjbpUkNUW5uAZ0vYi4BBgBbA/skVJaO/v4kuVdviRJzY25VZLUVJXlYBn/BK4BfpNSmj2vMaU0NiJ+l4PlS5LU3JhbJUlNUi4K0N2A2SmlKoCIKAFapZRmpZRuycHyJUlqbsytkqQmKRfXgD4NtK7xvE22TZIkLRtzqySpScpFAdoqpTRj3pPs4zY5WK4kSc2VuVWS1CTlogCdGREbznsSERsBs5cwvSRJWjJzqySpScrFNaAnAfdExNjs817AgTlYriRJzdVJmFslSU3QchegKaXXI2ItYE0ggA9TSnOXOzJJkpopc6skqanKxRlQgI2BlbPL2yAiSCndnKNlS5LUHJlbJUlNznIXoBFxC7AaMAyoyjYnwCQpSdIyMLdKkpqqXJwBHQwMSCmlHCxLkiSZWyVJTVQuRsF9H1ghB8uRJEkZ5lZJUpOUizOg3YDhETEUmDOvMaW0Zw6WLUlSc2RulSQ1SbkoQM/OwTIkSdL3zi50AJIkNYRc3Ibl+YjoB/RPKT0dEW2A0uUPTZKk5sncKklqqpb7GtCIOAq4F/hXtqkP8ODyLleSpObK3CpJaqpyMQjR8cCWwDSAlNInQI8cLFeSpObK3CpJapJyUYDOSSlVzHsSEWVk7lUmSZKWjblVktQk5aIAfT4ifgO0jogdgXuAf+dguZIkNVfmVklSk5SLAvQMYCLwHnAM8CjwuxwsV5Kk5srcKklqknIxCm41cE32R5IkLSdzqySpqVruAjQiPqeW61JSSqsu77IlSWqOzK2SpKZquQtQYHCNx62A/YEuOViuJEnNlblVktQkLfc1oCmlb2r8jEkp/R3YfvlDkySpeTK3SpKaqlx0wd2wxtMSMkdt2y/vciVJaq7MrZKkpioXXXD/VuNxJfAFcEAOlitJUnNlbpUkNUm5GAV3u1wEIkmSMsytkqSmKhddcE9e0usppYuXdx2SJDUn5lZJUlOVq1FwNwYezj7fA3gBGJWDZUuS1ByZWyVJTVIuCtBuwIYppekAEXE2cE9K6cgcLFuSpObI3CpJapKW+zYswEpARY3nFcDKOViuJEnNlblVktQk5eIM6C3A0Ih4AEjAPsDNOViuJEnNlblVktQk5WIU3PMj4jFg62zT4Smlt5d3uZIkNVfmVklSU5WLLrgAbYBpKaVLgdERsUqOlitJUnNlbpUkNTnLXYBGxB+A04Ezs03lwK3Lu1xJkporc6skqanKxRnQfYA9gZkAKaWxQPscLFeSpObK3CpJapJyUYBWpJQSmUESiIi2OVimJEnNmblVktQk5aIAvTsi/gV0ioijgKeBa3KwXEmSmitzqySpSVquUXAjIoC7gLWAacCawFkppadyEJskSc2OuVWS1JQtVwGaUkoR8WBKaSPAxChJ0nIyt0qSmrJcdMF9NSI2zsFyJElShrlVktQkLdcZ0KztgGMj4gsyo/UFmQO46+Vg2ZIkNUfmVklSk7TMBWhErJRS+grYNYfxSJLUbJlbJUlN3fKcAX0Q2DCl9GVE3JdS+lGOYpIkqbl6EHOrJKkJW55rQKPG41WXNxBJkmRulSQ1bctTgKbFPJYkScvG3CpJatKWpwvu+hExjczR2tbZx/D9QAkdljs6SZKaF3OrJKlJW+YCNKVUmstAJElq7sytkqSmLhf3AZUkSZIkqU4FKUAj4vqImBAR79do6xIRT0XEJ9nfnWu8dmZEjIyIjyJi50LELElSY2ZulSQVg0KdAb0R2GWhtjOAZ1JK/YFnss+JiAHAQcA62XmuiAi7KEmStKAbMbdKkhq5ghSgKaUXgMkLNe8F3JR9fBOwd432O1NKc1JKnwMjgU3yEackScXC3CpJKgaN6RrQnimlcQDZ3z2y7X2AUTWmG51tkyRJS2ZulSQ1Ko2pAF2cqKWt1nujRcTREfFGRLwxceLEBg5LkqSiZW6VJBVEYypAx0dEL4Ds7wnZ9tHAijWm6wuMrW0BKaWrU0qDU0qDu3fv3qDBSpJUBMytkqRGpTEVoA8Dh2UfHwY8VKP9oIhoGRGrAP2BoQWIT5KkYmNulSQ1KmWFWGlE3AEMAbpFxGjgD8CFwN0RcQTwFbA/QErpg4i4GxgOVALHp5SqChG3JEmNlblVklQMClKAppQOXsxLOyxm+vOB8xsuIkmSipu5VZJUDBpTF1xJkiRJUhNmASpJkiRJygsLUEmSJElSXliASpIkSZLywgJUkiRJkpQXFqCSJEmSpLywAJUkSZIk5YUFqCRJkiQpLyxAJUmSJEl5YQEqSZIkScoLC1BJkiRJUl5YgEqSJEmS8sICVJIkSZKUFxagkiRJkqS8sACVJEmSJOWFBagkSZIkKS8sQCVJkiRJeWEBKkmSJEnKCwtQSZIkSVJeWIBKkiRJkvLCAlSSJEmSlBcWoJIkSZKkvLAAlSRJkiTlhQWoJEmSJCkvLEAlSZIkSXlhASpJkiRJygsLUEmSJElSXliASpIkSZLywgJUkiRJkpQXFqCSJEmSpLywAJUkSZIk5YUFqCRJkiQpLyxAJUmSJEl5YQEqSZIkScoLC1BJkiRJUl5YgEqSJEmS8sICVJIkSZKUFxagkiRJkqS8sACVJEmSJOWFBagkSZIkKS8sQCVJkiRJeWEBKkmSJEnKCwtQSZIkSVJeWIBKkiRJkvLCAlSSJEmSlBcWoJIkSZKkvLAAlSRJkiTlhQWoJEmSJCkvLEAlSZIkSXlhASpJkiRJygsLUEmSJElSXliASpIkSZLywgJUkiRJkpQXFqCSJEmSpLywAJUkSZIk5YUFqCRJkiQpLyxAJUmSJEl5UVboABYWEV8A04EqoDKlNDgiugB3ASsDXwAHpJSmFCpGSZKKiblVktRYNNYzoNullAallAZnn58BPJNS6g88k30uSZLqz9wqSSq4xlqALmwv4Kbs45uAvQsXiiRJTYK5VZKUd42xAE3AkxHxZkQcnW3rmVIaB5D93aO2GSPi6Ih4IyLemDhxYp7ClSSp0TO3SpIahUZ3DSiwZUppbET0AJ6KiA/rO2NK6WrgaoDBgwenhgpQkqQiY26VJDUKje4MaEppbPb3BOABYBNgfET0Asj+nlC4CCVJKi7mVklSY9GoCtCIaBsR7ec9BnYC3gceBg7LTnYY8FBhIpQkqbiYWyVJjUlj64LbE3ggIiAT2+0ppccj4nXg7og4AvgK2L+AMUqSVEzMrZKkRqNRFaAppc+A9Wtp/wbYIf8RSZJU3MytkqTGpFF1wZUkSZIkNV0WoJIkSZKkvLAAlSRJkiTlhQWoJEmSJCkvLEAlSZIkSXlhASpJkiRJygsLUEmSJElSXliASpIkSZLywgJUkiRJkpQXFqCSJEmSpLywAJUkSZIk5YUFqCRJkiQpLyxAJUmSJEl5YQEqSZIkScqLskIHIEmSJOXDJoMGMHrMmDqn69unD0OHDc9DRFLzYwEqSZKkZmH0mDGMPbdtndP1/n3dRaqkZWMXXEmSJElSXliASpIkSZLywgJUkiRJkpQXFqCSJEmSpLxwECJJkiSpwByhV82FBajUCJmEJElqXhyhV82FBajUCJmEJEmS1BR5DagkSZIkKS8sQCVJkiRJeWEBKkmSJEnKC68BlbTUHCRJkqT6qW/OnDljBlD3+A9SsbMAlTTf0iTJqZf0rHM6B0mSJDV39R1YsP1J0/IQjVR4FqBSM5DrwrIQSdKzrpIkScXPAlRqBprC0VdvTSNJakzsWistGwtQSZIkaSkV6uDu7Jkz6N29Y53Tza34jvIWrZY4jb2GVAgWoJIaTH2SpMlPkrS8mtPZyMrq6noXvhMv6rrEaew1pEKwAJXUYOqTJDuf/FG9juQ2hS8NkqSG0RQuNZGaCwtQSQW1NEdyJUmSVNwsQCU1Kbm8NgbsIixJhdScutZKzYUFqJRn9UmmJtJll8trY8DrYySpkOxa27Dqe9DWg7HKJQtQKc/qk0xNpI2HAylJkpqq+h609WCscskCVJKWoD7J2cQsSVL91bdrtQd4myYLUKmI1bfrjF16JUnSssp1V936dq32AG/TZAEqFTFHkJUkSQ3NrrrKJQtQKUccqU+SJDVn9sxSfViASjniSH2SJDUN9SmkLKIWZc8s1YcFqCRJklRDfQopiyhp2ViAStJyyvXgDI4OKEmSmioLUElaTrkenMHRASUVKw+gSaqLBagkSVIzlsuisb4H0Dqf/JGD1UjNlAWoJElSM1aIXhcOViM1XxagarbsJiRJkiTllwWomi27CSnfvD+aJEn1l8tB/jzx0HhYgEp1sJuQcsX3kiRJ9ZfLQf4c4K/xKCl0AJIkSZKk5sEzoCoq9ek+YdcJNRe5vv+oJElSQ7MAVVGpT/cJu06oucj1/UclSZIamgWoJDVxnimVGo4Dm0iFV5885wB/jYcFqCQ1cZ4plRqOA5tIhVefPFffAf48aNvwLEAlSQXj2SNJUmPSFA7aNvbcWjQFaETsAlwKlALXppQuLHBIyqH6/qHYfUJqWjx7VFjm1qbN3Co1nMZ8prSx59aiKEAjohS4HNgRGA28HhEPp5Q8HF5Pjf1ISH3/ULw/olQc/OLb+Jlbmz5zq9RwmsKZ0kIpigIU2AQYmVL6DCAi7gT2AkyS9dTYj4TkUn2PSPnFV1pQff526nuQKtdffBvzkeYiZm6VpCKS64O7hcqtxVKA9gFG1Xg+Gti0QLGokavvESmP+EoLqs/fTqEOUnmkuUGYW7VUHGlUWnq5LPJyfXC3ULk1Uko5XWBDiIj9gZ1TSkdmnx8KbJJS+sVC0x0NHJ19OhB4P6+BLp9uwKRCB7GUjLnhFVu8UHwxF1u8YMzLq19KqXuhgyi0ZpJboXG99+qj2OKF4ou52OIFY86HYosXGl/M9cqvxXIGdDSwYo3nfYGxC0+UUroauBogIt5IKQ3OT3jLr9jiBWPOh2KLF4ov5mKLF4xZOdPkcysUX8zFFi8UX8zFFi8Ycz4UW7xQnDEDlBQ6gHp6HegfEatERAvgIODhAsckSVIxM7dKkvKuKM6AppQqI+IE4AkyQ8Vfn1L6oMBhSZJUtMytkqRCKIoCFCCl9Cjw6FLMcnVDxdJAii1eMOZ8KLZ4ofhiLrZ4wZiVI80gt0LxxVxs8ULxxVxs8YIx50OxxQvFGXNxDEIkSZIkSSp+xXINqCRJkiSpyBV1ARoRu0TERxExMiLOqOX1iIjLsq+/GxEbFiLOGvGsGBHPRsSIiPggIn5ZyzRDImJqRAzL/pxViFgXiumLiHgvG88btbzeaPZzRKxZY98Ni4hpEXHSQtMUfB9HxPURMSEi3q/R1iUinoqIT7K/Oy9m3iW+7/Mc818i4sPs//sDEdFpMfMu8T2Ux3jPjogxNf7vd1vMvI1pH99VI94vImLYYuYtxD6u9TOtsb+XVbdiyq/m1vwohvxqbi1ozI02vxZbbs2ut2nn15RSUf6QGTDhU2BVoAXwDjBgoWl2Ax4DAtgMeK3AMfcCNsw+bg98XEvMQ4BHCr1/F4rpC6DbEl5vVPt5offI12TuSdSo9jGwDbAh8H6Ntj8DZ2QfnwFctJhtWuL7Ps8x7wSUZR9fVFvM9XkP5THes4FT6/G+aTT7eKHX/wac1Yj2ca2faY39vexPnf+vRZVfza0Fe480uvxqbi3ofm60+bXYcmt2vU06vxbzGdBNgJEppc9SShXAncBeC02zF3BzyngV6BQRvfId6DwppXEppbeyj6cDI4A+hYonhxrVfq5hB+DTlNKXhQ5kYSmlF4DJCzXvBdyUfXwTsHcts9bnfd8gaos5pfRkSqky+/RVMvcRbBQWs4/ro1Ht43kiIoADgDvyEUt9LOEzrVG/l1Wnosqv5taCaJT51dyaH8WWX4stt0LTz6/FXID2AUbVeD6aRRNOfaYpiIhYGdgAeK2WlzePiHci4rGIWCe/kdUqAU9GxJsRcXQtrzfW/XwQi/9AaWz7GKBnSmkcZD54gB61TNNY9zXAz8gcra9NXe+hfDoh263p+sV0XWms+3hrYHxK6ZPFvF7QfbzQZ1qxv5ebu6LNr+bWvCmm/Frsn0fFkluhOPNro86t0DTzazEXoFFL28JD+tZnmryLiHbAfcBJKaVpC738FpkuLesD/wAezHN4tdkypbQhsCtwfERss9DrjW4/R+am6nsC99TycmPcx/XV6PY1QET8FqgEblvMJHW9h/LlSmA1YBAwjky3m4U1yn0MHMySj9AWbB/X8Zm22NlqaWsM+1lFml/NrfnRRPNrY93XxZJboXjza6PNrdB082sxF6CjgRVrPO8LjF2GafIqIsrJvJFuSyndv/DrKaVpKaUZ2cePAuUR0S3PYS4c09js7wnAA2RO7dfU6PYzmQ+Kt1JK4xd+oTHu46zx87pXZX9PqGWaRrevI+IwYHfgkJRSrR9w9XgP5UVKaXxKqSqlVA1cs5g4GuM+LgP2Be5a3DSF2seL+Uwryvey5iu6/Gpuzatiy69F+XlUTLk1G0PR5dfGnFuhaefXYi5AXwf6R8Qq2aNxBwEPLzTNw8D/RcZmwNR5p60LIdvP/DpgRErp4sVMs0J2OiJiEzL/R9/kL8pF4mkbEe3nPSZzYfz7C03WqPZz1mKPaDW2fVzDw8Bh2ceHAQ/VMk193vd5ExG7AKcDe6aUZi1mmvq8h/Jioeun9llMHI1qH2f9APgwpTS6thcLtY+X8JlWdO9lLaCo8qu5Ne+KLb8W3edRseXWbAzFmF8bZW7Nrq9p59fUCEZCWtYfMiPEfUxmpKffZtuOBY7NPg7g8uzr7wGDCxzvVmROgb8LDMv+7LZQzCcAH5AZsepVYIsCx7xqNpZ3snEVw35uQybhdazR1qj2MZnkPQ6YS+ZI1RFAV+AZ4JPs7y7ZaXsDj9aYd5H3fQFjHknmOoN57+erFo55ce+hAsV7S/Y9+i6ZD+NejX0fZ9tvnPf+rTFtY9jHi/tMa9TvZX/q9X9bNPl1Ce/DRvW5v1DMRZdbszE16vy6mM/9Rv15tJiYG21uXULMjTa/1hZvtv1GGmFuza67SefXyAYpSZIkSVKDKuYuuJIkSZKkImIBKkmSJEnKCwtQSZIkSVJeWIBKkiRJkvLCAlSSJEmSlBcWoFIjFRFVETEsIt6PiHsiok0DrWdwRFyWfTwkIrZoiPVIktQYmF+lwrIAlRqv2SmlQSmlgUAFmXut1SkiypZmJSmlN1JKJ2afDgFMkJKkpsz8KhWQBahUHP4HrB4RXSLiwYh4NyJejYj1ACLi7Ii4OiKeBG6OiH4R8Ux2umciYqXsdPtnj/i+ExEvZNuGRMQjEbEymST8q+yR4a0j4vOIKM9O1yEivpj3XJKkJsD8KuXZUh3JkZR/2SOuuwKPA38E3k4p7R0R2wM3A4Oyk24EbJVSmh0R/wZuTindFBE/Ay4D9gbOAnZOKY2JiE4115NS+iIirgJmpJT+ml33c8APgQeBg4D7UkpzG3BzJUnKC/OrVBieAZUar9YRMQx4A/gKuA7YCrgFIKX0X6BrRHTMTv9wSml29vHmwO3Zx7dk5wN4CbgxIo4CSusRw7XA4dnHhwM3LPPWSJLUOJhfpQLyDKjUeM1OKQ2q2RARUct0Kft75hKWlQBSSsdGxKZkjroOi4hBS5iHlNJLEbFyRGwLlKaU3q9v8JIkNVLmV6mAPAMqFZcXgEMgc20JMCmlNK2W6V4m06WH7PQvZudZLaX0WkrpLGASsOJC800H2i/UdjNwBx6dlSQ1XeZXKU8sQKXicjYwOCLeBS4EDlvMdCcCh2enOxT4Zbb9LxHxXkS8TybZvrPQfP8G9pk3SEK27TagM5kkKUlSU3Q25lcpLyKlVPdUkpqtiNgP2CuldGihY5Ekqakwv6q58hpQSYsVEf8gM0LgboWORZKkpsL8qubMM6CSJEmSpLzwGlBJkiRJUl5YgEqSJEmS8sICVJIkSZKUFxagkiRJkqS8sACVJEmSJOWFBagkSZIkKS/+H5vC9xl/2kv3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_minimizing_cell_size = 1           # True - minimizing cell size, False - maximizing cell size\n",
    "number_offsets = 10\n",
    "number_cell_sizes = 100\n",
    "min_cell_size = 1\n",
    "max_cell_size = 5000\n",
    "\n",
    "wts, cell_sizes, dmeans = geostats.declus(df,'X','Y',feature,iminmax = int(find_minimizing_cell_size), \n",
    "                    noff= number_offsets,ncell=number_cell_sizes,cmin=min_cell_size,cmax=max_cell_size)\n",
    "df['Wts'] = wts                            # add weights to the sample data DataFrame\n",
    "df.head()                                  # preview to check the sample data DataFrame\n",
    "\n",
    "plt.subplot(221)\n",
    "GSLIB.locmap_st(df,'X','Y','Wts',xmin,xmax,ymin,ymax,0.0,2.0,'Declustering Weights','X (m)','Y (m)','Weights',cmap)\n",
    "\n",
    "plt.subplot(222)\n",
    "GSLIB.hist_st(df['Wts'],0.0,5.0,log=False,cumul=False,bins=40,weights=None,xlabel=\"Weights\",title=\"Declustering Weights\")\n",
    "plt.ylim(0.0,250)\n",
    "\n",
    "plt.subplot(223)\n",
    "GSLIB.hist_st(df[feature],vmin,vmax,log=False,cumul=False,bins=40,weights=None,xlabel=feature,title=\"Naive \" + feature)\n",
    "plt.ylim(0.0,250)\n",
    "\n",
    "plt.subplot(224)\n",
    "GSLIB.hist_st(df[feature],vmin,vmax,log=False,cumul=False,bins=40,weights=df['Wts'],xlabel=feature,title=\"Declustered \" + feature)\n",
    "plt.ylim(0.0,250)\n",
    "\n",
    "weighted_data = DescrStatsW(df['Porosity'].values, weights=df['Wts'], ddof=0)\n",
    "\n",
    "vmean = np.average(df[feature].values)\n",
    "vvar = np.var(df[feature].values)\n",
    "vdmean = weighted_data.mean\n",
    "vdvar = weighted_data.var\n",
    "\n",
    "print('\\n' + feature + ' Declustering Results:')\n",
    "print('Stationary Mean: naive mean is ' + str(round(vmean,3))+', declustered mean is ' + str(round(vdmean,3))+'.')\n",
    "print('Variance/Sill: naive variance is ' + str(round(vvar,3))+', declustered variance is ' + str(round(vdvar,3))+'.')\n",
    "\n",
    "cor = (vmean-vdmean)/vmean\n",
    "print('Declustering correction in mean of ' + str(round(cor*100,2)) +'%.')\n",
    "\n",
    "# print('\\nSummary statistics of the declustering weights:')\n",
    "# print(stats.describe(wts))\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare the Naive and Declustered Cumulative Distribution Functions\n",
    "\n",
    "Let's take a look at the naive and declustered feature cumulative distribution functions for a visual comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(111)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df[feature], facecolor='none',bins=np.linspace(vmin,vmax,1000),histtype=\"step\",alpha=1.0,density=True,cumulative=True,edgecolor='black',label='Naive',linewidth=2)\n",
    "plt.hist(df[feature],weights=df['Wts'],facecolor='none',bins=np.linspace(vmin,vmax,1000),histtype=\"step\",alpha=1.0,density=True,cumulative=True,edgecolor='red',label='Declustered',linewidth=2)\n",
    "plt.xlim([vmin,vmax]); plt.ylim([0,1.0])\n",
    "plt.xlabel(feature + ' (' + feature_units + ')'); plt.ylabel('Cumulative Probability'); plt.title(feature)\n",
    "plt.grid(True); plt.legend(loc='upper left')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to calculate our experimental variograms.\n",
    "\n",
    "#### Experimental Variograms\n",
    "\n",
    "We can use the location maps to help determine good variogram calculation parameters. For example:\n",
    "\n",
    "```p\n",
    "tmin = -1.0e21; tmax = 1.0e21; \n",
    "lag_dist = -999; lag_tol = -999; nlag = -999; bandh = -999; azi = -999; atol = -999, isill = 1\n",
    "```\n",
    "* **tmin**, **tmax** are trimming limits - set to have no impact, no need to filter the data\n",
    "* **lag_dist**, **lag_tol** are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* **nlag** is number of lags - set to extend just past 50 of the data extent\n",
    "* **bandh** is the horizontal band width - set to have no effect\n",
    "* **azi** is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* **isill** is a boolean to standardize the distribution to a variance of 1 - it has no effect since the previous nscore transform sets the variance to 1.0\n",
    "\n",
    "#### Dashboard for Interactive Experimental Variogram Calculation\n",
    "\n",
    "Below we make a dashboard with the ipywidgets and matplotlib Python packages for calculating experimental variograms.\n",
    "\n",
    "* allowing you to calculate the experimental variogram interactively while changing (and exploring) the search template parameters.\n",
    "\n",
    "* first calculate the directional variogram(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the experimental variogram\n",
    "l = widgets.Text(value='                              Variogram Calculation Interactive Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "lag = widgets.FloatSlider(min = 10, max = 500, value = 10, step = 10, description = 'lag',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update = False)\n",
    "lag.style.handle_color = 'gray'\n",
    "\n",
    "lag_tol = widgets.FloatSlider(min = 5, max = 500, value = 5, step = 10, description = 'lag tolerance',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update = False)\n",
    "lag_tol.style.handle_color = 'gray'\n",
    "\n",
    "nlag = widgets.IntSlider(min = 1, max = 100, value = 100, step = 1, description = 'number of lags',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update = False)\n",
    "nlag.style.handle_color = 'gray'\n",
    "\n",
    "azi = widgets.FloatSlider(min = 0, max = 360, value = 0, step = 5, description = 'azimuth',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update = False)\n",
    "azi.style.handle_color = 'gray'\n",
    "\n",
    "azi_tol = widgets.FloatSlider(min = 10, max = 90, value = 10, step = 5, description = 'azimuth tolerance',orientation='vertical',layout=Layout(width='120px', height='200px'),continuous_update = False)\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "bandwidth = widgets.FloatSlider(min = 100, max = 2000, value = 2000, step = 100, description = 'bandwidth',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update = False)\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "\n",
    "ui1 = widgets.HBox([lag,lag_tol,nlag,azi,azi_tol,bandwidth],) # basic widget formatting    \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def f_make(lag,lag_tol,nlag,azi,azi_tol,bandwidth):     # function to take parameters, calculate variogram and plot\n",
    "    global lags,gammas,npps,lags2,gammas2,npps2\n",
    "    tmin = -9999.9; tmax = 9999.9\n",
    "    lags, gammas, npps = geostats.gamv(df,\"X\",\"Y\",feature,tmin,tmax,lag,lag_tol,nlag,azi,azi_tol,bandwidth,isill=0.0)\n",
    "    lags2, gammas2, npps2 = geostats.gamv(df,\"X\",\"Y\",feature,tmin,tmax,lag,lag_tol,nlag,azi+90.0,azi_tol,bandwidth,isill=0.0)\n",
    "    \n",
    "    plt.subplot(111)                                    # plot experimental variogram\n",
    "    plt.scatter(lags,gammas,color = 'black',s = npps*0.03,label = 'Major Azimuth ' +str(azi), alpha = 0.8)\n",
    "    plt.scatter(lags2,gammas2,color = 'red',s = npps*0.03,label = 'Minor Azimuth ' +str(azi+90.0), alpha = 0.8)\n",
    "    plt.plot([0,2000],[vdvar,vdvar],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol < 90.0:\n",
    "        plt.title('Directional ' + feature + ' Variogram - Azi. ' + str(azi) + ', Azi. Tol.' + str(azi_tol))\n",
    "    else: \n",
    "        plt.title('Omni Directional ' + feature + ' Variogram ')\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.5*vdvar])\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.0, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "    return\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'lag':lag,'lag_tol':lag_tol,'nlag':nlag,'azi':azi,'azi_tol':azi_tol,'bandwidth':bandwidth})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Variogram Calculation Demonstration\n",
    "\n",
    "* calculate omnidirectional and direction experimental variograms \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Calculate interpretable experimental variograms for sparse, irregularly-space spatial data. Note, size of the experimental point is scaled by the number of pairs.\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance\n",
    "\n",
    "* **number of lags** - number of lags in the experimental variogram\n",
    "\n",
    "* **bandwidth** - maximum departure from the lag vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f95c0e9745f6480c94d743e66d2174bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                              Variogram Calculation Interactive Demonstration, Mich…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85b529d1ac384f368b2d75322c7230be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 432x288 with 1 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dashboard for Interactive Variogram Modeling\n",
    "\n",
    "Below we make a dashboard with the ipywidgets and matplotlib Python packages for modeling experimental variograms.\n",
    "\n",
    "* fit the directional variogram(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n",
    "l = widgets.Text(value='                              Variogram Modeling, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "nug = widgets.FloatSlider(min = 0, max = 1.0, value = 0.0, step = 0.01, description = r'$c_{nug}$',\n",
    "                          orientation='vertical',layout=Layout(width='60px', height='200px'),\n",
    "                          readout_format='.0%',continuous_update = False)\n",
    "nug.style.handle_color = 'gray'\n",
    "it1 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description='$1^{st}$ structure:',disabled=False,layout=Layout(width='200px', height='30px'),continuous_update=False)\n",
    "c1 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.1,step=0.01,description = r'$c_{1}$',\n",
    "                          orientation='vertical',layout=Layout(width='60px', height='200px'),\n",
    "                          readout_format='.0%',continuous_update = False)\n",
    "c1.style.handle_color = 'gray'\n",
    "hmaj1 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 25.0, description = r'$a_{maj_1}$',orientation='vertical',layout=Layout(width='60px', height='200px'),continuous_update=False)\n",
    "hmaj1.style.handle_color = 'black'\n",
    "hmin1 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 25.0, description = r'$a_{min_1}$',orientation='vertical',layout=Layout(width='60px', height='200px'),continuous_update=False)\n",
    "hmin1.style.handle_color = 'red'\n",
    "\n",
    "it2 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description='$2^{nd}$ structure:',disabled=False,layout=Layout(width='200px', height='30px'))\n",
    "c2 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.0,step=0.01,description = r'$c_{2}$',orientation='vertical',\n",
    "                          layout=Layout(width='60px', height='200px'),continuous_update=False,\n",
    "                          readout_format='.0%')\n",
    "c2.style.handle_color = 'gray'\n",
    "hmaj2 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 100.0, description = r'$a_{maj_2}$',\n",
    "                          orientation='vertical',layout=Layout(width='60px', height='200px'),continuous_update=False,)\n",
    "hmaj2.style.handle_color = 'black'\n",
    "hmin2 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 100.0, description = r'$a_{min_2}$',orientation='vertical',layout=Layout(width='60px', height='200px'),continuous_update=False)\n",
    "hmin2.style.handle_color = 'red'\n",
    "\n",
    "ui1 = widgets.HBox([nug,it1,c1,hmaj1,hmin1,it2,c2,hmaj2,hmin2],)                   # basic widget formatting   \n",
    "#ui2 = widgets.HBox([it2,c2,hmaj2,hmin2],)                   # basic widget formatting   \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def convert_type(it):\n",
    "    if it == 'Spherical': \n",
    "        return 1\n",
    "    elif it == 'Exponential':\n",
    "        return 2\n",
    "    else: \n",
    "        return 3\n",
    "\n",
    "def f_make(nug,it1,c1, hmaj1, hmin1, it2, c2, hmaj2, hmin2):                       # function to take parameters, make sample and plot\n",
    "    nug = nug *  vdvar; c1 = c1 * vdvar; c2 = c2 * vdvar\n",
    "#     text_trap = io.StringIO()\n",
    "#     sys.stdout = text_trap\n",
    "    \n",
    "    it1 = convert_type(it1); it2 = convert_type(it2)\n",
    "    if c2 > 0.0:\n",
    "        nst = 2\n",
    "    else:\n",
    "        nst = 1\n",
    "    print('Ignore this warning, since we are kriging the original feature the sill is not one:')\n",
    "    vario = GSLIB.make_variogram(nug,nst,it1,c1,0.0,hmaj1,hmin1,it2,c2,0.0,hmaj2,hmin2) # make model object\n",
    "    nlag = 100; xlag = 10;                                     \n",
    "    index_maj,h_maj,gam_maj,cov_maj,ro_maj = geostats.vmodel(nlag,xlag,0.0,vario)   # project the model in the major azimuth                                                  # project the model in the 135 azimuth\n",
    "    index_min,h_min,gam_min,cov_min,ro_min = geostats.vmodel(nlag,xlag,90.0,vario)   \n",
    "\n",
    "    plt.subplot(111)                                    # plot experimental variogram\n",
    "    plt.scatter(lags,gammas,color = 'black',s = npps*0.03,label = 'Major Azimuth ' +str(azi.value), alpha = 0.8)\n",
    "    plt.plot(h_maj,gam_maj,color = 'black')\n",
    "    plt.scatter(lags2,gammas2,color = 'red',s = npps*0.03,label = 'Minor Azimuth ' +str(azi.value+90.0), alpha = 0.8)\n",
    "    plt.plot(h_min,gam_min,color = 'red')\n",
    "    plt.plot([0,2000],[vdvar,vdvar],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol.value < 90.0:\n",
    "        plt.title('Directional ' + feature + ' Variogram - Azi. ' + str(azi.value) + ', Azi. Tol.' + str(azi_tol.value))\n",
    "    else: \n",
    "        plt.title('Omni Directional ' + feature + ' Variogram ')\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.5*vdvar])\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.5, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'nug':nug, 'it1':it1,'c1':c1, 'hmaj1':hmaj1, 'hmin1':hmin1, 'it2':it2, 'c2':c2, 'hmaj2':hmaj2, 'hmin2':hmin2})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Nested Variogram Modeling Demostration\n",
    "\n",
    "* select the nested structures and their types, contributions and major and minor ranges \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Fit a positive definite variogram model based on the addition of multiple structures each describing spatial components of the feature variance \n",
    "\n",
    "* **nug**: nugget effect\n",
    "\n",
    "* **c1 / c2**: contributions of the sill, in percentage of the sill calculated from the declustered variance\n",
    "\n",
    "* **hmaj1 / hmaj2**: range in the major direction\n",
    "\n",
    "* **hmin1 / hmin2**: range in the minor direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3aaf0877fb2b4a619b39606d4df1c5ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                              Variogram Modeling, Michael Pyrcz, Associate Professo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "043a7acba3514628ba9c0f7d7e9b6caf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'stream', 'text': 'Ignore this warning, since we are kriging the original feat…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kriging to Calculate Spatial Estimates\n",
    "\n",
    "Now let's build spaital maps with our data and modeled variogram.\n",
    "\n",
    "We require a grid for our map. \n",
    "\n",
    "* first we specify the extents of the grid with xmin, xmax, ymin and ymax.\n",
    "\n",
    "* then we specify the number of grid cells in x and y with nx and ny\n",
    "\n",
    "* then we calculate the required size of the cells in each direction, xsiz and ysiz, and the cell center of the grid origin, xmin and ymin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = 100; ny = 100                           # number of cells\n",
    "xsiz = (xmax-xmin)/nx; xmn = xmin + xsiz*0.5 # calculation for the size of each cell and the cell origin\n",
    "ysiz = (ymax-ymin)/ny; ymn = ymin + ysiz*0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform kriging with specific kriging settings.\n",
    "\n",
    "* **skmean** - if performing simple kriging this is the stationary mean, use the declustered mean, vdmean\n",
    "* **ktype** - kriging type, 0 = simple kriging and 1 = ordinary kriging\n",
    "* **ndmin, ndmax** - the minimum and maximum number of data for each kriging estimate, reduce ndmax to speed up the calculation, increase ndmin to avoid making predictions with too few data\n",
    "* **tmin, tmax** - the trimming limits to remove data outliers\n",
    "\n",
    "We look at the kriging estimates and the kriging (estimation) variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ignore this warning, since we are kriging the original feature the sill is not one:\n",
      "\u001b[0;30;41m make_variogram Warning: sill does not sum to 1.0, do not use in simulation \u001b[0m\n",
      "  Estimated   10000 blocks \n",
      "      average   12.000093435166745  variance  7.704696215375236\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skmean = vdmean                            # simple kriging mean (used if simple kriging is selected below)\n",
    "ktype = 0                               # kriging type, 0 - simple, 1 - ordinary\n",
    "radius = 500                              # search radius for neighbouring data\n",
    "ndmin = 0; ndmax = 50                 # minimum and maximum data for an estimate\n",
    "tmin =  -1.0e21; tmax = 1.0e21             # data trimming limits, set very small and large to not trim the data\n",
    "\n",
    "print('Ignore this warning, since we are kriging the original feature the sill is not one:')\n",
    "vario = GSLIB.make_variogram(nug=nug.value*vdvar,nst=2,\n",
    "         it1=convert_type(it1.value),cc1=c1.value*vdvar,azi1=azi.value,hmaj1=hmaj1.value,hmin1 = hmin1.value,\n",
    "         it2=convert_type(it2.value),cc2=c2.value*vdvar,azi2=azi.value,hmaj2=hmaj2.value,hmin2 = hmin2.value) # variogram\n",
    "\n",
    "kmap, vmap = geostats.kb2d(df,'X','Y',feature,tmin,tmax,nx,xmn,xsiz,ny,ymn,ysiz,1,1,\n",
    "         ndmin,ndmax,radius,ktype,skmean,vario)\n",
    "\n",
    "plt.subplot(121)\n",
    "GSLIB.pixelplt_st(kmap,xmin,xmax,ymin,ymax,xsiz,vmin,vmax,'Kriging Estimate','X(m)','Y(m)',feature + ' (' + feature_units + ')',cmap)\n",
    "plt.scatter(df['X'],df['Y'],marker='o',s=10,c=df[feature].values,edgecolor='black',cmap=cmap)\n",
    "\n",
    "plt.subplot(122)\n",
    "GSLIB.pixelplt_st(vmap,xmin,xmax,ymin,ymax,xsiz,vmin,7,'Kriging Variance','X(m)','Y(m)',feature + ' kriging variance (' + feature_units + '^2)',cmap)\n",
    "plt.scatter(df['X'],df['Y'],marker='o',s=10,color='white',edgecolor='black')\n",
    "\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.1, wspace=0.3, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spatial Predictions with Kriging\n",
    "\n",
    "Select a location and observe the kriging estimate and estimation variance.\n",
    "\n",
    "* We assume Gaussian distribution for the uncertainty parameterized by a mean of the kriging estimate and variance of the kriging estimation variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kriging Estimate = 10.03, Kriging Estimation Variance = 0.32\n"
     ]
    },
    {
     "data": {
      "image/png": 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0Vu6x1l5kjPk0/sN8L7HWvt1mu6dpo9QaYyy+arUgW2/KWvvZ7GtfAr5ord07+/sU4DkxgGzbrjeBy62197RVao0xBv+D1D7W2oXZdYcAN1lrR+WrT1EURVEURVG6Gp3hVHoafW5akD7x3DRFURRFURRF6Q50wKn0NPrctE5guv+5aYqiKIqiKIrS5UR6uwHKnoe1doUx5hjgWWPMldbar3VgmyXGmNbnpn0fmIp/j8R9nWjC7fix3ofix5bv0nPT8Ad38+gjz02z1iattZ4xpvWZWZdZa9cY/3lok621j3RRm5Q+yAknTrPr123u9PazZy94xFp7Yhc2SVECVFdX25EjR/Z2MxRFUZQuZvbs2eustdvZdDrgVHoFa+0yMehsAf7egc0uBK7F119fwX9uWrsPd86z77nZ+yVvwR/I/Z6OPTftq+L3FmttNX7wxN/wZ0wbsm2Sz027zhjzBeA/1tov72xbRZvnGWNan5vm4T+nK+y5aV62bd/GDwl6KZvQuxz4K74CrPRT1q3bzMuv/L7T20fdU8M0bUXpEkaOHMlrr+XLjFMURVF2Z4wxS/Ku19AgZXfF6HPTFGU7pk4bY196+bed3j4WOXO2tXZaFzZJUQJMmzbN6oBTURSl/2GMyfsZQu/hVHYb9LlpiqIoPYsx5kRjzHvGmAXGmO/kef1b2TCzN40x7xhjMsaYqt5oq6IoitI36bYBpzHmGmPMGmPMO2JdlTHmMWPMB9l/K8Vr383+h/aeMeYEsX6qMWZO9rU/hiSbKnsG+tw0RWkPC9Z6nf5RlFaMMS7wF+AkYCJwfvY5xtuw1v7aWruftXY/4LvAM9baDT3eWEVRFKXP0p0znNcCbYMnvgM8Ya0dCzyR/Z3sf2DnAZOy21yV/Y8O/HvOPoc/KBibp05lD8Fae5+1dpi1tshaO85a++9dqOt/RHLrTGvte13ZVkXpPSwemU7/KIrgQGCBtXaRtTaJf9/7GTsofz5wc4+0TFEURdlt6LYBp7X2WaDtt5xnANdll68DzhTrb7HWJrL30C0ADjTGDAbKrLWzsrNP14ttFEVRlHzYTOd/FCXHEILPO67PrtuO7COrTgTu6IF2KYqiKLsRPZ1SW2utXQlgrV1pjBmYXT8EeEmUa/1PLZVdbrteURRFyYPFYm26t5uh9A/y3cISduvBacALYTqtMeZz+LYSw4cP75rWKYqiKLsFfeWxKGH/qe3Mf3aB/9DiJjJ1ULxi+41Dtg6/ea8jt4y2f+ufrEXehWpCtpVlHFHGMWLZ8Vjc3IhXUYFtbKDGGsqjsTb7tWK5A43rAIE2h2xrrcm7LPvK5F/dpkeMWMq/39D1xu5UmTACZdqUD+zDdGAfge1FIXn8NqxTw8rk72vrdf5259Bj6cC5DzbU5FsbXC+Ww85HWJ/IY7SiQesTSdY6LqaokOimLexVXBDWWAA2JFKscRxMURGRTZvZq6hw+/2KpnlivSdEkY7cBZzvSFYnG9mcSuj96Upfox4YJn4fin/fez7OYwc6rbX2auBq8FNqu6qBiqIoSt+npwecq40xg7Ozm4Pxn30I4f+p1WeX267Pi/wPbWRhjf3BqDOy63Of4zIhy8EPkLLO9j8Dhn1Ilr5y1MnVGpHLIQOWmChTEMnNVhRHk7nleAv3r17GrxbMobKwkL9NOoSaePCDdcTJKXJOSDtDB03yWBxZxhPL+cun07lLKyWWZXlXHKMNORdyvSzviGXXzR1jRCy7IcuRaK4/HTd//wT6RBy7G00hiRbkzocTVm8st42JiKtLDpYyuceJeqncsvXkYEb0USrXp14mVyadjG5bzshlL789L/tUIo/ZEdefG8mIMvm3ddzc+kxIO61YzojrQ24bNniWx5JK5L5gkddcfVMz577yDls3beZnU8Zxct12zyAOsKK5hXNffodNGzdx+aTxnFZX69efzvVhOp07L82p3H4TokxSlLEhI3JXvH/cbD9f+s7jO2zfzmGxnqqxSpfwKjDWGDMK/zm+5wEXtC1kjCkHZgAf79nmKYqiKLsDPT3gvBe4CLgi++89Yv1NxpjfAXX44UCvWGszxpitxpiDgZeBTwJ/6uE292lOrR3G8TV1RIyDowG+igLA0KJCnpsxjbS1xJz2b1WvKyzgmRlTSVuLa6Ptlu/bWNDwH6ULsNamjTGXAY8ALnCNtXauMeaS7Ot/yxY9C3jUWtvYS01VFEVR+jDdNuA0xtwMHAVUG2PqgcvxB5q3GWM+AywFPgaQ/Q/sNmAekAYutXZbesUX8BNvC4GHsj8donWGJHQmM2y2M08d/jGFzP6FlHFDlmWd8k4rJ2R9WswGJZ3cDAqJ3GymE0lta3fYrNXOsrOzmp4nZunELJRsjwnph+B5yT9AiETkjKWYdQuZ+YxE5Ixj++2XhM1qutHgvXFGzmR2ZFZTEFBeRf9iHLGYWx82qylnQeWsaeAY2LlrQh5zRB6LE3LuRXuCs8Oi3wMz12IGNXBu5HKu/mQ6N6OYbIlvW06lcoPDfNeTATJiX16IsSCv14SYbZaz83ImU85wJkX5sL8XbshxbSvTld8VWbAa/qN0EdbaB4EH26z7W5vfr8X/f1pRFEVRtqPbBpzW2vNDXpoZUv7nwM/zrH8NmNyFTVOUXmVtoz8YqI5351OJOs6GRJq0ZxlYuLvP7Ck+FjwNDVIURVEUpW/QNz7xKsoewiMLEkz6x0Ym/WMjjy9u6e3m8PKaBg5+8F0OfXg+9y/b2NvNURRFURRFUfoZfSWltsux5NTYjqizYRqtJw3CQIBNfl1WhgO5HVA3w0gJxc8LOU0yMTQScp9axMl/vMEyUkntfDiQ7DcnJBCoI+FA8rgK4olcO6WyGqbOymNx5XL7imFAgQzRaGVoTtvXnKgM1JGRprlju3NBmgHnHgYZj3vmvcFxo3091IaosIEAIaFuBtRkeQxu/nNJSGLtY6s2Ez3mENyqcu55+UXOHFMMBMOQpDZMSPiQ7GsC56B9V9SGBDSlmoU6GxIOlJZhS57sHxFcFKJoe6JPZD1pUU9LMrffZqHUpjyp0ebqDHvPOyFhWa2qbVhadeewqtQqiqIoitJn0BnOPs6GVAMtmVT7BZVdYm1Lio2J7tcQL5oYY+OtL7Dx9ll8Yu94+xt0M2ePKMd76iWa/vsQnxpV2tvN6RRbU2nWtCTaL9hJGtMp1iaauq3+bsGmO/+jKIqyBzFy2CCMMTv8GTlsUG83U1F2a/rtDGd/4L71b/PAhjlEjcOPR55MTWz3HBD0dR6s38jXZi/FAW44fBQH1pR0274OHRpj2aXVAEQ7MPvX3UyuKmLuWXvjWUtBvGvCpnqSD7Y28MnX3yQDfH2v0ZwzZGi72+wMy5q3cun8Z0lby4WDJnL2wPFdWn+3YC3oY1EURVE6xJL61djf7LiM+ebqnmmMovRT+u+A03bsGZr5kBpt2EfwmHxmplQ9RZmOPJ8zjJQ1zGpYwqgZF7H+3eeY27CaIyrK/WD6LPI5f1IbjJhM3jJh2qokTKMNJOt2QKOVOmc6LfTgMI02RDmMCl1WptQ64vmiUnMNe65mGMZY7l61haKPnURmw2YeWzqPQ4fG2ui4Ynm7lNqQBFqhaMoEWuPY3EBT6stiWy8ltOOw9Fc35PxJlTlEf5W40TSt86yB+kWfBnTfsPVt6mxFDnukbuqE9FuyMZe8LBNowxKQX1i3kfikKcQm7M39jz/KRwf7j/O1Nv+ftsAzeUU9UqOVCbSzNqyiYPg4SiZN54mnHuCkyu3zy8JSofM9bxPA6VJ9Nh8WVKlVFEVRFKWPoEptB9mUbiDpba+2bkw1kOymRMhjysax4Kl/kllfz5Tirp256Y+sbUmxObnz5+Ki4eU03fYg3lMvcvbIsm5omdJdHFUzEO/duWy47WbOr+l65Wlq+RAy9Yuov+86Tinfq8vrVxRFURRF6e/03xnOLuShja/w4ta5xEyErww+m7KIH6xy34bXeGbzXAqcCN8fdhoV0eIu3e+MigkcUjaGQsfgGP1uYEfcu2w9335rCY4x3HTYXuw/oONa7JGDyph7xmRiBSmiTu9rrkrHGV5UxOOHHEbaWmKm6/+c1cSK+eeE00hbD9fG2t+gj2B0hlNRFEVRlD6CDjjbwVrDG40LmXjI/7Bs7oPUJ1eyb2w0BW6G15sWMfr4z7Jm9oMsalnF9NheoVPGUu8MU22l4hfPanpxxyHuZmiVe6W+J7XYME1P1hnYVuqNTv62Sf1Q6o0SJ0QnzGTa12jD2iNTZ6MyLTYiNVqRHOt43LVmE8UXnEZ6+WqeXrGQ6bWFAeUzoP4KnbM1jTYCOK6lNfM1kEwby7XB7CDtNixpNpBY6+Q/T15KaMoyjTawrUjBldeQTCgOaLT52yq14I4ouFLfleWl3i3V4kCbw7RmcT5atuS+HMiE6NphGOMRdSGKIZ3euTTkZCa3rxah0TaK5WTGBVwMQb0+7P0WVMPzv688qcJnz3Vn9f+86D2ciqIoiqL0IXTarAMcWjKRt5//K7ZxI2MKh2xbf3TpeN5/6CrM5vVMKKrrxRYqnxpeTuON9+I99yqnDq/o7eYoewDNmRQbUn0xvdZqSq2iKIqiKH0GneHsADPK9+OQ0kkURYJq6/FVk5lRMZ4Cx1HltZeZWVfOO2dMJuKiWqzS7axKbOU7ix8hZT3OGziZM2sm9HaTAqhSqyiKoihKX2GPG3AGkkulahcolPutIJs0WYxD1PFo1RRbh5eFTjSYvimVug5otFKli7pCJZUPhxfLcVEmFkhtza+2BpJHkbqf1FlzyzINNJ0WamcHjkWmh3ZEo5VtlhptcVFu1iig0br5k2lb218cCba/PY3WX86vzsr1UheV2iyAlSmyMnnV5D8fgW2lRhvQcfOrxpKAsiserxLQcWVibyR/e7x0+1+UyPY4UdHvoo+cENVYWLcBbTixKfeIH5nE21Vqadj1J9NowzTadIgOLnmncSUFQ8dQM+Vgnn7qHs4a6A84ZTKt/Fsg1faMONet6dIW/ZJEURRFUZT+iU7LKXlZl0jQlFa9LowNzRk2tex+z61Uuob9S+tIL1/Msvv+zSkVo3q7OUGsBc/r/I+iKIqiKEoXssfNcCrtc0t9PVcuWkDMOPx7n+mMLO544uuewMOLmrn40Y0Y4PZTB3DYkIJ2t1H6F9WxYq4efzopz6M46ra/QQ/TnUqtMWYYcD0wCD9L6Wpr7R+MMVXArcBI4EPgHGvtxjzbnwj8AX8C/J/W2iu6rbGKoiiKovQ6e/SA0+mAChumxUZC1gf0WmSZ3H6tsCSjQkWMSWVU6qZiOeam85aRhB6X1HSF/plukwx6z7p1VJ5zAck5b/HyprXsVVoUSLKVqm2YuigJ02hlAm1c6KyxeDK3rdSGIzJdVaR+Sq1VdK7UaANKsNhWarSRgtx+ZZJrIO3Wi3DrogQVFxxDpqGZexa+xeHDfR1TKqxGKqxy+1CNViqy7c8sy/KBeuL5E3WtFdqq0GhtJirKiHpkf4l+dGLi3BS35MpE8w9wvOacqpppKMzb5o7gdaC8Cbnuk0KjTYi05WRG6tftt8Fpoze7xsF1HVyTyVsmJa7LjM2/3Lrfjuy/43R7Sm0a+Ia19nVjTCkw2xjzGHAx8IS19gpjzHeA7wDflhsaY1zgL8BxQD3wqjHmXmvtvO5ssKIoiqIovYcqtcp2nFs9iA233ID3/nyOqK5hQ3L31ms3JtJsTXX8A/iG5gybE+Fq4UXjCth805M03TeLc8f1vdnNTS0eG5pVjdxjsfgDzs7+tFe9tSutta9nl7cC7wJDgDOA67LFrgPOzLP5gcACa+0ia20SuCW7naIoiqIo/ZQ9eoZTyc+pg4ZwTHUt8QjcvWoFv1v4PjHX5fr9pzKquLi3m7dTPLB0E19+tR7XwK0zRnJA9Y7bf/+CZj7z6CYcA3efXc4hQ2PblTlqeAGL/mcQBkPh9i/3Ks8vS/CRuzZjLVx/WgknjY33dpOUHsZgMbZnvnAwxowE9gdeBmqttSvBH5QaYwbm2WQIsEz8Xg8c1N3tVBRFURSl99gjBpxtVbhWpIIXDVFPA/WE1C+TKU2I+SfbUCDU0IiTm1GICnUxGkijbX92MewYHZNfZw3TYluPvTjqYozl3vVrqbjgAlJvz+HZNRsYMrQ8kPQZmkAr9hsRWmw0ItRZoWfGChK58kKpjQjlVZ6vMP3Tij70Ui63rdhC8bmnkF69nkeWv8fUmkLcSP5kWiee4tYPWxj8mRkk1zdw36J3OHSEC07uzLsRv23t3tXq5U+OlYQpuKYDCbcSJ5o7/nsXN1N99nSipQXc8vKrnDzeHxHbVP72yH604ppApgALfdcRyq5bkjtnpkQozhtz9WRaxIA3LIE2RJfNpPO3Myw5NrCtSIWVGm1KXLvyfRsJe5ROyKkIe78lM7k/qR5SN8/fttZrt0uN2l2n2hjzmvj9amvt1W0LGWNKgDuAr1prt5iwP35tNsuzro8dvqIoiqIoXckeMeBUOs851bX8/MYbKYxEOWKfA3u7OTvNJ4eX8vwt9xNxDKcdM7Ld8hdPiHLBv54m4hg+dm5pu+X7GudOinL9ja/iWfj92d0f9rS1xZLMQHUfz5VqzqRJeBmKnOCfvJZMmhYvTbG7e83ct8uu3cO5zlo7bUcFjDFR/MHmjdbaO7OrVxtjBmdnNwcDa/JsWg8ME78PBVbsSmMVRVEURenb6IBT2SGnDa7jmJqBODZK1Nn9bvk9bmgZc8+egGsg7rbf/uNGx1lyWTWu41EQ2f2ejXjg0AjLvl6GZ6HQ6d639+ylHsf/LUPGg39e5PCx6X3z+ljavJVL332OpOdxydB9OKVmLwDqW7by1fefJull+HTdvpxSPaaXW9pVWOhGpdb4U5n/At611v5OvHQvcBFwRfbfe/Js/iow1hgzClgOnAdc0G2NVRRFURSl19kjBpxh6mxUpraGaLFh6lwYUveLiDoLRSKrTKOVSm1cprZG8qukMi1WJneGJcRK0kKllMcVEe2R9Seziapx49KUitPaIqnUytRceSwxkfIqNdp4PKdhxoVGGw0s55RaqZ1KAqmu4lgyyZw+mcmuL42BI49RtM3JkyZb5BqMa/OWN210ayv0SE8m0HZEow1Lte0Ask2B9mQcCqL+cXjJ/BqtbGcmkeuvQP2yTqm8iuvsvvkZJp01gurRZdx4/zw+OilbZ6MIUhLberINAa03t7dUKpp3/c5e34l0rp5XNi4jPnYiFeOn8OjTj3DWoOEAvNWwgvjo8VRO3J8nn3yQNC2sTjXzkQETeWbTUlammvhY9SQGxIr84wpRfxPp/H9Gvbz2aDC92s1ex1361Yalu5+neRjwCWCOMebN7Lrv4Q80bzPGfAZYCnwMwBhTh//4k5OttWljzGXAI/iPRbnGWju3OxurKIqiKErvskcMOPsLG5IJSqIuBW7vPfdvcyqBS4wCVy+dPZ2z9nf4yx+WkExbrv+4SxcPm7qMgysH85/5z7Jl/ttcOHLf3PqKQVz/7nOsev8dThgwnNua1hIdMoJ3Fr7AhliU6OCRrFoxmx+NPGLbNgnPV3Aro305jMliuvGxKNba5wk/2TPzlF8BnCx+fxB4sHtapyiKoihKX0NHDbsJ19cv4arFCymKRLjpgGnUFRa2v1EXc//qD/nj0neIOS5/nHAkwwvLerwNSt9hv2EO9b8wpDNQHBa60wcYUlDCf/c5kZTNUBzJ/cmrKyjmv/seT8pmeHnjGp7dsBibSeNgsF4Gm0nhCN1hdWIr3170GAkvzScG7cup1eN643AURVEURVF2K/rvgNPkVMu40CBjMhXWkRrt9pobBPU3idTl0uIB71JVLYqKFFaR+il12UJZRqS2Ss01Eslw7/pVVH32UySfeZa3GlcydkAtGaEQyuWU0AmTQp+UdUaFvivbnA5JMPU8hwc2raL25Ato/uAdXtu8klFFJcF2iuV4LL86G6bRRsSyI5JmpcLqRIWCKpTSTHPu2SRG3KcZCUvubZNku229TPSN5ldqaavUNudmumxaiKhS0XbC6hJasJs/FVbuz7j5NUkrrj8rFNkwdVauD9Qjz3cmRBUWybHJZeU4QAzIXU1BXVbuV24r608mYnnXZzL57wmV7Uwkc9tKjVaq7dFIGv8VQ9QV7zHxt+DUokoa2cqKLRv5+KT9eGLdKlY0buYjI6dQEvWvzXfWLSc+dAzVEw/k8Wfv57jKidn25G1mAPk3JSLTqLNtMDup7rdLDz0WRVEURVEUpT3674BzJ9icbqbYjRJ1Oq+qNmdSzNq8hCHxcsYX13Rh63zOr6vm11dfQ2k8yuGHTtq2PpHxeHDFGuoKiplWVRHYZmMyieuZLlNwT68YwpUP3ERBJMIhE45of4NuIO1Z7nqvmYpiy/Gje05r9DzLXfNSxKOWUyZE2JIAx0A/yzbdhrWWx5Y20pj2OHvvOK6YwWxIWtKepaKge0KCnluziQ2JFMcNHNRjQVWOMZw/dATg36N8zpCRADQkcvejHlA2mBsXPEn9knl8ctD0HmlXp7Ddq9QqiqIoiqLsDHv8gPOmNW9z/7p3KY3E+c3oEyiLFLS/UR6uXPESHxZGaF72Fj8efhR7FQ3o0nZeNHIwZw2poTACMfEh/DvzFjErEiW1sJ6/ThrP1Oyg8+b6ZVy5cAGFboTr9jmIusKiXW7DCTUjOKyyjrhriO3C4HxX+PGsBm5cGyG9JcHvEx4fmdgzavGvXkhy1QcxMokM5y1s4Zo3PBwD957jcsiw/ME7uzP/XbCF78xtwimM815jgu8fWA7A6ytSnHhLA2nPcs2pxZw5oWsH/Y+tXM+35q/ALSvj9c1L+P74UV1a/65QGy/h33ufQtJmwOvjz4HRGU5FURRFUfoI/XbA6WC3qbRSo5UJsVHH46nNixhxzmWsf/JOFras4pCKoUSkxilUN6npycTKqGNYndpK0X5H4yWbaGATpfGSQIKrTKAtEkmtMaHURoR2GxV6bSzuLxcDrqjHZhzqU404048g+vrbrElvpaCggEyTw51r11Bz3kW0zH6Z2Q0rGV05hHhMJt/mjlGmgUqkIltS0Oz/u21NarsyBeK4Couac8de0JI7rkKp0eaOURLQaEWb3cIkCxvTlEwfS2LZBhZv2v4xfwFdVr4g02SFquk67X8wt55hwUYo3X8Yma0Jnpy3jLqLDyO1tYX/vvsOBw0qzLZbqrBCow2k0cqUWi/vesQ1Z2IhabRJobkK5dXrwHJHEl8XN6RwxwzFGVDOgkXvbtvmzjcd4jOnUTpkANc+9TwnD/avCKkKS8U3I/abEupsWizL9ixtSOIOHYIzbChL5szJ1SPOXzJEo5X1yPMaldeoeF9Foy1c8+FyViSSfHGvwdy3ch3LWpJ8dugwHly9lmUtCS4cOJbH169ieaKF0yqmMCBWRBSXRhlu3IE02mhA5Zcp1X5FXXsHrMV0b0qtoiiKoihKh+m3A86OcnzlKO689U9UxAqZVLt/p+v5ct2+XD37OSYWlnFQeV0XtjA/G1rSFDoRfrVvDd9+8XlGFBRw8pDB217/WG0Nv7n5WoqiUQ6bvl+3t6en+NkRMS55dC4VMYfPHNdzQusPDo/y2QcXEncMHz8wyiXXPodr4GMfqeixNvQkn5tUwesvrKBxRT2XH1m+bf1po4q5+sHZNGY8Ljqua2fxAT42fBCvzF3MujfX8p1dnN1MZDIkvAwDQpTyh1au5R+btmJGDueN199jRSyOM2I4b7w9jxWxOGbUCF5/9w3WuFEiQ0eyqP51fjLy8F1qk6IoiqIoyp7GHj/gvLB2MqcPGEtJ1CViOn+/2OTSgfyxdGBgdrS7uHr+Bn7+xhqKYy6PHDuKR2YOp6khOPg6b+gQTq4dSGHECSi4uztjB7g8cX5xaPBNdzG8wuWRC9xts4knjY1jgELT/3RagKoCl9tmDgSCs5f71hTw3oXDSXmWiuIuDroBSqIR/rrfWCA8NKgjLG3ayiXzZpHIpPnm6ImcXjssf0FrwWbnKK3dlgBkrcXZlgZkO5YM1Ffo/udwKoqiKIqidJh+O+A05HQ1mUYr0yJbldcBrptNjvTLyfRKqeZJZU+mSkrtVq6X9RRKrTSS+wAfEcvRuNBoZZqrWB8tbubG+s0M+/5H2frAa7y4cQOfGFRORjx8PpLVUMvbtC2MtNhWqrZOyLFLZLJuQWF+dTYq1rtS65UprWGJrWJZptFamaabyZ9+GqaROm5u2Qqt1QbSVcnRsv19iq13jtqQNFrZj6HIBNq46Jeo9DVFcmwiRKNNhmi04rwG9iX0VORiSJtTTbn7mo3jEcMl5oCXkdm0Yr/iHCQacvcOy/bIa84LGVh6op2JRO4cNKdi+YoH3quvbF5FdMJESiZM5t4nHuIjdf7svys099OGVbI+3cKydav54rQx3L9yA8s2rOLifSbx8Oq11K9Zy3njpvHE+pWs2LCBU+umbbuO5Pgz7O+C1PfDliOaUqsoiqIoSj+n3w44+zOfGhfn8p/9l5K4y9Gn75q+uymZJmod4m7fngXd3OIR8SyF0b77vMddpSHhDzpKd9NJ06aUR8qD0ohDU8oj7Vl6Lkc4yGFVA7lmzks0zH2bz42dkLeMYwyf3st//xhjuXBELS0Zj5hXwCdH+DOiWxoLObduLwDWNxaS8nxN19CXT5LF6IBTURRFUZQ+gg44d0O+sG8J548vIpaJ79JA8cZF6/jhm8spirrcfcQERpR0LqG3u7nj3RY+92AjMdfw8McqmTKwL3/Y7xzPLMhw5r/92a67PuNw9Pi+/QVAW95em+CUe9eQyli+N7WCX72xhVTG8udD6jh1RHn7FXQxw4tKuH/60SQ9j6p4+19SLGls4SMvzKMpneY748bwsaHbf5GzOtHANxY8TksmxYW1Uzm+anx3NH3XUaVWURRFUZQ+RL8ecLamRAYSK2VCpNApZaJsTCqvsnyk/WfbOTIRVyTKukKjc0U9UkmNSHVWJLhGhJLaqn2WR1084UMWljfkyggNU+p+ViiK1hpuWLGJykvPIzXrTV5Yv45x1TUBxTeTyR+2IsvINseERusKJVjeAxia5BrNn8aaaY7x73mW2i+cRMuCldz/3gImVRYG9EypkWZ2Mo3VpELUTtFXMkHWiQTb6Yh3kJUf8sV9s4aQBNpobv0t71j2vnhfjDHc/NYcjs6OZWxGnL+UUIcTuUG3l5Sqsdiv1DTFoVmkNi1SmEVfpJP5B/WyR6WCfP/CJgqP3Z+KEQP59y1P4x42jfjwOv7zxDOcNLgaCCbNhp2bdDpXZ3NL7gsQmUYbprA7bXT2iAPFOEQiqbxl5PX9RH0DzvjxVE49gFvvvp9TBowGoCmZm6N9dfMS4kPGUD3xQJ549n6OrZqwXRuigb81ueVIIDVXaPRuNym1iqIoiqIofYTdaxqlF9mcSpLqZ7MGnxxZzKarbsGb8wFH1ZX1dnNCuWhchNV/fYimJ9/mpL26/7mbW5MeTamePdfn7Qvzr3uLd699k/P33f0GHyePKqb5sTdZe/UjfHx4CZnnX6Phhnu4YGhpbzetQxxWXUV6/ntsvP4GzhwwKG+Z/UrraFm+gGUPX8fRpX3n+aDb4wchdfpHURRFURSlC+nXM5xdxT+WLeaaJQuoLCjglgOmURXLH1qyu3HRuEpOH1FKzItT0Ifv4fzIhEJmjowT8SIURbu3nY8taeSix9fhOoa7TxzE1Nqe0YyPGuew9Mf+rF9pwe53n+q+A+O8+8khpD1LQbKUT40fQMpaim33f0HQFQwvLuLJww+iJeMRzeT/8qU2XsLV488g4aWJmp57JE+n6GdfjimKoiiKsvvSbwecxuQSaY34/O6KJM7AckB/y6mORYXN3LW2npqvfJHmu+5lTsNqThhcE1ACpWLqhmi3rkyjFfqoTG2VabSuWLYhaqvUViVh5SWZrDJZEwMvk9uXI7RKKVW6IW0OLBeKeoRCTEgCrRPL1WmFSinTaLH+ALMi7pBuieFlNwnTP71U+8culeOAOuoZrntvMyXnn0Bm41buWDCPfStLA28S20Z9lB/rpV6LJ8qJJkk918gU4IxDSbYpNiGTacWxJXL9khHr5fEElF2ZTCsbLdojE2hTifxfpEjd04pHB8ljTzYX42artp6h0HEoBNKpcKV727ZCCZYabdrLNdQNedyQweYtE6a/e2K/LU25BN1WZdcBtgqNtkWo2xnr4BqHIjdKxpNab67+sL8pUuWXyn48q/t2JE2641idqVS6DGPMicAf8N/e/7TWXpGnzFHA7/H/21hnrZ3Rg01UFEVR+jj9dsDZlZw9aCDX/PGvlMejTNtrn95uTofZnMj4z+F0DZsTHgURQ9zd/WbPepLzhpbw5E2P4DiG044d0tvN6RGa0x4ZS68lyu6JpDyPFi9DvDv+AmtokNJFGGNc4C/AcUA98Kox5l5r7TxRpgK4CjjRWrvUGDOwVxqrKIqi9Fl0wNkBvjx6OBcOHUR5zCHWh9VTyb/mNvDd5zdQGnf54uRifjV7CyVxl6fOHsjwsv6X8tpVHDe0jDfPHE8snqYk2v5s6e7OG2ubOfPhlWSs5Z+HDWHm0L57L29/YW2imU/NmcXWVIIv7TWOC4cM7+0mKUoYBwILrLWLAIwxtwBnAPNEmQuAO621SwGstWt6vJWKoihKn2aPGHA65NffJFJ/i8dFwmp2fU1hJKvO+r9HhGIqlVqpCjoiBVemm0ZClFRZ3hNarNRTnahMPBWzGDaoOl7zQTN13/4YzY+/zjXzP2TE985i6+Nv88TyNfxPTUlAt5TJrgFFVhyj3K8jknWlRhtA6pxy27joK5nAKpRaK45FqqOBZak6SqU2k/8LAXleMi1u3vWxrMZcGnFxHYOXyVOnE6xfXjdS53XkuXHy65KBY044edcHlNqUjMQVabQh9UP+9anG3H2VdyzYSOTYQyioKueGF2ZxzOBKv07RfrErMrJt6fxzogHFV64XfZ1oyW3b1JxrT1pc94b202jletfNP6snr6fG5pyymxTXUHMqp/UmRRsSMg1ZtF/+HemIDivb+fqm1TBiGNVHzeD2W2/j40OHtbv9TuPtfsFTSp9kCLBM/F4PHNSmzDggaox5GigF/mCtvb5nmqcoiqLsDuwe03XKTvOJMTFW/up2knMW88mJcZb+4i5a3lzMMSO7LgRna8IjmdYPtt1Nc8rSlOqefj51WBmpx2fReMtDnLebJMru7kyvHIC3ZAnr/3UNZ1bXdsMeNKVW6TLyfXvU9o9RBJgKnAKcAPzAGDNuu4qM+Zwx5jVjzGtr167t+pYqiqIofZY9YoZzT+QL+5ZwzrhCSgo9CqOGz+9XRFEkQmG0a+7hvOmdFr70cANFBQ7PfrqQMQP6v37aG7y4LMmZt2/FWvjvGRUcObxr77ScWlPEW2eNJWOhzO0f6ct9nUEFhTww7QiaMmkGFHSD3q73cCpdRz0gp+CHAivylFlnrW0EGo0xzwL7Au/LQtbaq4GrAaZNm6bfVCqKouxB7HEDTivDQ4UOGRMPh5frXaGzxkUqrEyjlaqgK+sRZcJSXqXaGtBoI1JnTYvy4gDEsk2L1dGsBhyF1i+oBxS5+J9Es8m9IjwoGs0dl0yRDSTKxoX6W5jkX+9l2PcHx7LuhcU8uGAJX6kLfnB2CnPlA1eZ/BycCVEvhToamkYrVUehvKblepGKmhQJrAGNVujBaaGLOk7+89s2Adhz8uudwUIydTZkUCfLiGO+aW4j5R85DBN1ueGN2Rw5LKufmvwDChMy7k9tzT3GI9Ui2uAZSlx/fzas+SF9HXq8gozor5RIo00k8w+cw9JoHbHeEX0edp7kfpvFvhKp3DUkE2iTnrjOMlJPF+deHK5cn7HSrxVlAgpubuNkJoIhQrEbpyW1fdldxQBGZyqVruFVYKwxZhSwHDgP/55NyT3An40xESCGr9xe2aOtVBRFUfo0e9yAU+kaLppi+fr/PUHchRM+H6MhYYm5EIvs/AfnhoTFTVvindi2v/ORMTFuveNFrIVzT63o7eYouwVW7+FUugRrbdoYcxnwCP5jUa6x1s41xlySff1v1tp3jTEPA2/jf6X4T2vtO73XakVRFKWvoQNOpVN8+sAIp010KYrBvXMzHPTnFEVxw7OfjzG2puO3Bt/xVoZP3ZKhMJbhyYsL2XugqrmSGcMLeO8z/lMGKgr0lmtFUXoWa+2DwINt1v2tze+/Bn7dk+1SFEVRdh/67YDTktPUMiHpkhGhjEp1Vup7MaHROiEpmJG4SG0NUWGlRotUNIX2ubMarcREdk6hC6vfyPWR/EptKwPL/PZc/SZM/u6xbHjlQx5cvJCvj3OhUFxazSKZtkkcezLC31+Did+cwaY5K7h3zoeMP7wgkEYrNVorVUexnBF9KLXK5qb86afyvNuQcyGTTYOJweH9LPVoqd5mWtofKHppUUZer65HeSxblxG6c6haLfRikcga1o9poZhKBTyZzK/+ykRWG6KMpqS2KtrQ3JJbDts2kEYbos4GkoFFPVKnlhptk1gOaLTyHNn85yjjmbxl5FVgQ3TYYnEsibS4pj1xPWUV97A6OoXew6koiqIoSh9Cp0yUXeaT+3jM/eUTbHhuISdMNjQmbIfTaz85Gd793VOse+I9ThjbtYE4+WjOZEjqh3Gli0lbj+bM9l/K9BqaUqsoiqIoSh+h385wKj3Hpw9yOXWiQ0mVx/1vexz8iwzFhRme/d8Y4wbv+DuNC/ePcPxYl2iigNJ4937/8dzadXxj7lwijuG6qfuwd5k+BkTZddanGvjxkgdpSrfw8cHTObZyfG83KTgFqyiKoiiK0ov03wGnhXRWkSwQRxn2kHap0RYUtrRbvdRfpUbryjRXsT60HqmwuiHKZAcwIrU0TLeU+yIkDdQIZVQqtQEios5iv/ygUqA4xj9eTXHWzyaz6MW13Pfqar5xciSQoOs159TCTJOvWFY5kM4Ukm7y16dFimpGJMdK/VOmkMoyiURulrRt8ud/166h7NTTSa9fx0P1H7J3WWmHVEbrmbzLAFbsW+qXyMkuL/9AOqCVuvkTjQO6s1yWWrDoi4BGK/pRKsieUDoDSmpLru/ClFfc/GqrVHClytyS2LlZ64h4zwT02oBqm9tvRiiyLSnRhsCyUHwz7f/Jk30i02gDAcvinL69dTmx2pEM3PsQHn/xHk6o8h9BKPVdV5y7qNCDI9lj6Vql1upMpaIoiqIofQZVatvQlE6TCXs+hNIuFx4I9/5oDu89sYoT9ulbl9dpAwaw9YH7aHlpFjMHDujt5uy2JDMeLeneHdCkPI/mTMgXInlIW4+E1/4XQJ1h7+I6mlctYsmT1zOjdES37ENRFEVRFGV3pf/OcHaCv3+4lL8tWMygkgLunzGeiph2z87y6aOinLp/hELSlBb2rcecHFdby7TKSgoiltKontvOMG9TEx97dgEpz+Pqg0cws668x9vwYUMT5706h+ZUmp9OnMBJg2p3WH5VooFvLXyCplSCzw85mCMr9+rS9gyMlfGbvT5Ki5diUEHIc1Z7mm58LIox5hrgVGCNtXZydt2tQKtLXAFsstbul2fbD4GtQAZIW2undVtDFUVRFEXpE+wRn7oDSZNe/kFQxM1w64pVVH/3izTefA8vrmzk+LoBRKTeaESKrFBPpaoa0GilJieVwKhQZ92Oz9K0xZEKrtBfA0mz0fxlQvHyp7YG6w+Z3drqe6QDnWz+SLN/zN5WocI25HTLjFA40035E1XTidwH+IQsL3RFTyb9hqjCrfrnwIiTVTX9302IYh2mOLZdL3VeL5NfYQ30qc2fShwtyCUdI9VnqUqHaLSpRtGnIuE3kxJ9JPorLdqcFH1qOpBAe9/STbgzDiQ+uIZrnnyRQysGAdDUnGtDQui1TkgCrUSm0YaVkTy2ciPOlMlUjJ/AjQ8/ztGVw/1jkQm04hhnbVxBbPgEKkdP5uFZj20bcLYmxEJQnZWCQyDFWCzL43KNJeq4lOAiJ35lPdEQxbVV37V08Zcz3WtpXAv8Gbg+tzt7buuyMea3wOYdbH+0tXZdt7VOURRFUZQ+Rd9yHnuZUwdVs/HXf8ddvZYDqvpvoExT0pLO9D9tOJHJkNIE2m7l2EEVpJ55hcab7+esmopeacNh1QNIvjWHzbfexqlVNe2W37e0jqYP57Hy8Vs4qnR4D7Swt7HdmlJrrX0W2JDvNWOMAc4Bbu7aY1IURVEUZXelV2Y4jTFfA/4H/4lxc4BPAUXArcBI4EPgHGvtxmz57wKfwdewvmytfaQ72lUecUknU8TjUSJO39JBu4rrXs1w2e1pKkocXrgswvDK/nGcT69Zx9fenk/Mdblx+mTGlRX3dpP6JfsPKGXWcVNIepYKt7D9DbqBsaUlPHbYwbRkMpRQ1m75oQXl/G3cGbR4Kcqje8B14T+EuLf2fgSw2lr7QcjrFnjU+FPZf7fWXt1zTVMURVEUpTfo8QGnMWYI8GVgorW22RhzG3AeMBF4wlp7hTHmO8B3gG8bYyZmX58E1AGPG2PGWWt36IdaIJPV1VIilVMitbim5kJuWLaGQV/9Ck133sWLq5o4dlANrlRJhd7ohCTKStUxkDwq0msJS6DdQRpqvn0FEmiFkumU5pZNm1vK/jrbcNgvZlL/0Ps89MFyLpnhQrL9/cq2kRbJnakQ9bRF6JwydVZqsWK91GhToozUaBsaS3LrRfmykgZuXr2OknPOJL1yFQ8vW8KEipHIXFGpQ7oyiTdE4Qxb72WC11JGtEPqtSmRjCr12mg8d25i4noKPd8RUUam0QoF2RPqrExtlRptSpRJCU1Zpv16IWm6klSTX08M2CASaNPiPWYCummu/TLVVqrP8n0V1gapy6YyERz8b6iaRD8n0uJcCHU54nqUui6luMg/G4EU4w6kxEac/NdTSirdAYU4f9qt1Pqd7BdbfSynrNoY85r4/eqdGBiez45nNw+z1q4wxgwEHjPGzM/OmCqKoiiK0k/pLaU2AhQaYyL4nxtXAGcA12Vfvw44M7t8BnCLtTZhrV0MLAAO7OiOEl4Kr4Of5k6srmX9VX/FWbuW/Sp3LgylJe1x94IG3lmXbL9wL3LhAR4vX/4Ua19dzjETOnb6m1PbK7gZz3LP2xle+bBnFVbPWp5du5Y3NweNvtOrK2n47z2kXniFo2uruny/1lqeXL6V51c2dnibRMYj2Y7im8x4JDPhZay1PLU4wROLEtheHJWkPI/ETqTC7gl43Zh8u8vYXfiBddbaaeKnQ4PN7N/zs/FNlfzNsnZF9t81wF3sxN9yRVEURVF2T3p8htNau9wY8xtgKdAMPGqtfdQYU2utXZktszL7DTjAEOAlUUV9dl273L3ube5d+zoDCyr4+agTKHJ3nCD5tVFjOH/wEAaVehS6+WdFw/js0xuZlS6kZeVaHj29hknVfSStsg1fmenwsamWkgiUFrQ/q3PTG2kuuSNFebHDc5e4jBrgD1K/eZ/l9iXFNK9v4tYLMhwzbuf6q7P89cPF3LZpM+mWZi4fMZ4Z1X5ozal1Azl4QAWFUSjrhgTav767gd8vacamM/zfpBTnjKnYYfmX127h0y9/gGMM1x44nv3z3BP88ppGLnhmCcYY/nv8YA6s3V5RvfatFn4wOwPG8P19PS7Zv6irDqnDzN/SwEWvzSHpZbhy8mQOq67u8Tb0NTakGrl8yUNsTTXzidpDOLJiXG83KUjvKLXHAvOttfX5XjTGFAOOtXZrdvl44Cc92UBFURRFUXqe3lBqK/FnLUcBm4D/GmM+vqNN8qzL+2nKGPM54HMAAyLFPLFpPuPP/Bqrnr2ND5pXMq1sKMJyo0XoeJHsw9hr40UUus3tHkdArUzAO+vTxM8/GPvALD7YkGbvyoKAQiiVyYAWG6bR2rDZR6EoFrXklivFzGpp7riIbD8QrCsHmhJiv0IDlBpqs8tfZxsmff8ENjz7Afe+tYwvHRbFpCxvrUhRe9JENs1exrvL1nL0sCheUuicIi3VE6mlKZFSKzXaTFKmqOaWE0LbbErEmdfQQmzS/jjr1vBh8xZmujWks+eiIlJIxMnQOhFnQhJSpcIZhtRjAd7ZlMadPhnb2Mw7a5Zz1hC/XUlxbGlxPd24ZDWFJx6PTaa4/b15TC71Z11luvFNH26l4MxjwHrcOPcNpg/0B5MyHfitVVA4fSxEHN7+cOG2RFqp0Qb7Mb/iK6/XsAReSUqcg7uWbiY67WAKB9Rw/UuvMjY6drvyre8f8BOfW3HcXL9nOqDsSpJCkU1JnVgue/K4cttKlVdqrlKjldqtFTP4nvgjERPHJdXZ17YsJzpwJHuNO4jHXrqfw8rH0xbZz3K/UfH+b+23jqTzdhRrO5T902mMMTcDR+Grt/XA5dbaf+Hf+nBzm7J1wD+ttScDtcBdWdU4AtxkrX24+1qqKIqiKEpfoDdCg44FFltr1wIYY+4EDgVWG2MGZ2c3BwNrsuXrgWFi+6H4Cu52ZNWvqwFGFtbYMSV1PHf/VRQ5EcYO6t5ZmV9Pq+T/3fIYU6uinDAyXOlsTlkKXItj2v/Q35aWtCUe6dy2oXUmLRHP4oaEJF0wEb53xWNEHTj2M7nB3y9nOvzPva8zthQuPD6ad9vu4AtDRnP5269THnU5e9wkkp6H43lEnO61w78+oZr3X5tDzBj+Z9rQdsufXF3J4w8/BsAJ+03IW+b0ulLuu/spAM44elDeMl/dr4Q3nnwfz8LXj+p6VbgjHF5Zy+2vvoSHZeawA3qlDX2NCcV13LH0DTav/IDTqvbt7eZsTzfq19ba80PWX5xn3Qrg5OzyIqAPdpaiKIqiKN1Jbww4lwIHG2OK8JXamcBrQCNwEXBF9t97suXvBW4yxvwOPzRoLPBKR3Z0QfU0TqiYwJBCh7jTvYd69NBiXh1aHHgmZ1v+9mYT33tqM7XlUZ67sJya4o5rqNfMaeSbT2ympizCsxeWM6hk1xXWG17M8PlrM1SVOjz/VYfhVdsPOisLLOlUhsIih8JI7vWpQ13e+ELPaLSSvYrL+M+UQ4hHUjy/bg3/7923iUciXDd1H8aUlLRfQWf3Wxbn0WNGBZ+vuQNmDKzmscPLMMCAgvzX3tF1Zbx8yngMhkFVqbxlhpVFePJM/8uStoFFPUWB6+JYyHgeJW7PfbnQl6mNlfGrUR+hyUtRGem+605RFEVRFGV3pzfu4XzZGHM78DqQBt7An5UsAW4zxnwGf1D6sWz5udkk23nZ8pe2l1ALvkZXFM1QFC2kIJKm1cKVX/x35KH0UouTyqQVOp4byw0WAom1Mg3VsVw1J8H4K85l/U0v8OSHmzlvcgEExhBCD5RHaDz+8k6CkT86h633vswTy1Zx0dQ4TrnQaMtzs4+U5u4XtAXivkARYGPijfzlxa2ccMXBLHp4Mfe/v5pLj3Oxm2UqqsOfZzuM+94pbH7+A+6dU89lBwfvTbXpXD9IzTOwnA7RP6XyKdTItExdFRpjcUFOdTbGctv6lZSefRbplSt5ZEU9Y8aUBM7XzmqKMiE1kEKayt9maKPUZoJJreWR1iTZ3JcQUpv2Mg6V0Xh2Odfvycb2HzciB74ZeV16+TVOiUyIlUiNtqE5d6/ofSsWUjz5YGIV1dz59uvsXew/yzLq5H8bSlU1KvRaS/62pYU6K9NoZX/KOjMhib6ueO/JUy/DlkxeQz9osMdMrs0tGdGeNkpwgRujwI0RYvgHiAjFNyb6JBZpVWrbrWLn0MfRKoqiKIrSR+iV53Baay8HLm+zOoE/25mv/M+Bn3d3u7qbc8ZG+dPld1DgwGGHtP/8QMm5Y6L89v/uIOZajjxs155/mEhaYp7lvIMjXH75S0SNZeb38s+enbc3/ODXDxMxcMwn+tZMzskDqph99z04Bo7Yb0pvN2e3Iu1ZPCyRDgRVTy+r44F3nsUDzh0yrfsbp+waubRZRVEURVGUXqdXBpx7Kj84tJiLJsUZUGIpie3cPYf/78Bizp8Qp6YmQWm889MhNz7ewCW/X0dNhcOzP6/g7INjlKUbKC/KX+elB0U5Y4JLMTHKC3rrKTr5OW3wIA6uqiTuGMpjqnp2lLmbGrngpfmkMx5/nzaeQ2sqdlh+75Ia/jHhVNLWoyLa8ym5SifQAaeiKIqiKH2EfjvgNNhtyp8jPn1F3PwPog/TL9sqlHn3JRQ5qTrKLW1WDxxW4mLcDPmk4ECSraizNY12VAlEKnLJtKZUnL7i4lwbKnLhMl5JRW45VshVj73M9/5Ux9P3bOG+d2NccvYAnJXiKQYbc9qqzR77kGIXLxHBZs3QMHXWCgVSrg+mpQoFV6bRtgglWBCNCB1VaJity4MKWweaO/6E7Tj5X5cardQQM/JYRBmpnUIwUVdeQxHRbrleHoPcR7KpYNuyTLINQ14rYRpxIJ1VbHvP8vW4Mw4lVlHGDbNe5cBK/x7R5pZcG5qSufMRcTJUxfzjzIj4Uzck7Ve+32QSdEE0p54nhEYrlzMBrTlXp1Rq5X4DGq04Xjfs/Szqd8SbUOrBzTIdV5QPU5QDqb/yGhB/X+LiepDLrcdiunqEGKIdK4qiKIqi9DT9dsCpBEkkPdyI5ezjavnFtxYRMfD7C/Mnoyr9k7RnsVhm1pZz09MvAnDyxDG93Kqex7OWjPWImJ4NYWrd764J8R1EZzgVRVEURekj6IBzD+DG+1Zz2U8XUFsT47H/HMhZx9dSlV5PeRck3Sq7B+9sbOT8F98n41n+MX0szx6zD2lrqXD61n253c3qRCNfef9pGpItfH3kdI6uGt4j+92YaubrC55kU6KRL43cn1NqRvbIfhVFURRFUXqbfjvgNOR0O/lQ+qCOl18JTHfg8RMy6dMNlM9pg4EHv6dEmXT+eyHlI1WceK4et1hotEVi6qIgl5Bqi4RSKzTa1IBR/OPuufzzb5X8984E97+U4n8umkB0tbdNs3TEMyxtRrQ5kVMLPZHG6qVl+RBdNrAs03dz69PpYKrrtuONZPKuDyTZdkB1lnhCMZTHm5HtDKizuWOX+8q0SSqNuO0GJgeVS0FKJPamTe6tKJVaVyrFYt+uVK4j7bfhrvr1uDMOJ1Zawn9eeY0/TPE12sam3HxbSyp3jsPU3Iy4/MKSVVPiPDlChZXqrESmv8r+9USirFRe5X4D7QyZ1pP1vLK5HmfUOGon7svdTz7MCTVDAGgUx54QybQZ+R4Wdcr18oqISo3WzZ27Vzctx9YNoe6gI7n9/ts5faA/0I1lz+/OJirvEIsqtYqiKIqi9Bn67YBTyXH6ySP50tfexnEcfvitwaRSHq5ncZzwD6WpjMWzFqfLn9eg9CQZz2KB4waVc/PTzwNw8qTRvduodvCsn6DrdIPyekDZQP793nM0LnyX0waP7/L6w5hSWkPivXdorP+Q82r36vb9WX0siqIoiqIofQQdcO4BfOlzkznj5JGUlRfwyOP1HHb8PQwcEOGJf01haO32YT23vpHhM7dkqCzezDMXljKsXNXb3ZF3NjTxkac/JJ3x+PchY3j++ElkPEsZO/dInp5kRcsWvr3oKZrTCb4x4jAOKh/apfWPLCrj5inH0ZhJUxvvucTdwQUlXD/5RLamkwwvyh+Q1aWEzKoriqIoO088AqYDX8CPGFrLh8tW9UCLFGX3ot8OOI3JpdTGhHK4sxptIHnU5ldhjdB0pfYYSHCVCqjUcYVGayK59U48mSsfEbqdbIJUYWO5D7GZonJRxt/v8OHl4GX45/Xv8Oeryrj3rgQPPrWez59TGzgWm3b5/SzDyG+fSsNz73Lf/BV8YVoskIpKnrRY/4Bz7TEiPVRuK7XYqDj2jNQ2pYIrk3tFP0fElSu12IDGLBNJRZ+nZIKu1GhFompKaJUynbRtMqvcXl5bYe1IJvNrq/L6k8m8udJBvbYj3LpkE+5xhxItKeL6Wa/z5/0rANi6NVerPE5JmJ4qkSqs7BfZX2nyp7ymvfzvpec21hMbPYXyIXtx/2vPcUi5r7xGQt63sp1hWmpM6LieNZTFIpQRoUVo7knR/1KXlec3E3Ldy/Mu29k2KbfYjVLsRnGdJN2KNTrgVBRF6UISabC/ab+c+ebq7m+MouyG9K0HKyrdRirlYa3ltBNH8v++0cALzyeZMT3/TNc5Ez3qf38/W19bwFEjYnnL7OlkrCVj+3YU6PGDS0k8+gLNdz7OKQNLe7s5HWLf0sE0fPAma565kyNK63q7OYqiKIqiKMou0m9nOJUct925kEu/8TyDBxXy8O0ncfpJI6hJLKOyLP/p/+rhEc6c6FHUXEZlgX4n0ZY3Nq3na+++jus4/HXSVMaXlre/US9wxKBSXjp5HGlrqTR9V6OVjC4awF/HnUaLl6auoEceINIvsRoapCiKoihKH6HfDjgNEMsmeQZSaoXyJh+2LpM4w1JFpdJpQhQ/L6B35hTNgJobyyXQGpFIakxI0odcLe8hiIqUWqHUOslmUX4j//j3W35K7R0tPHzfXD57/jCiaxqhIVsmKdoT8/tq1CBIb8gA/u/yA2xQcxWzfEJdtqn8l1a0IJE7rEz7g9mwc+GJPgm7r0JuKzVdqa8mku3rpfIYLYbb166k5NgT8ZqauHfhYr5RUpltlNh3Sqbc5uoN0z6ldivbHYvn1y8D6b0hSnGiJU5rdnFDc27wJtXhMGSyqxeikialQt2BgB85ISyTbKWOG3U8qmJRIBp430rke1W2MyLqiQn9OBOSPpwMpNGK9GSpQHv5lWCZRiuXZdsCyctOfuXYtC539WS5KrWKoiiKovQR+u2AU8lx6rFD+MrX38dg+NEXqnqtHWnPEnF2/+TbYyqqef6JRwA4Yvx+vduYLsCztsvHO0ovoo9FURRFURSlD6EDzj2Ar312HGeeMITKojSV5e3PbnUHDy/dwuefX05pQYQHTxjGyLLd997QY2oGM7msAtcYBsQKers5u8SChi18Ye4rpLwMV4w7kAPKa3q7SUoXEGYGKIqiKIqi9DT9dsDpGEs84quiETcTWJ+PYBJpftXWCVHnZPKqsVIzFOWjQg+UaaYywVVoqKZZKLLxnPKKIxXWXHmnRWi0jivWNwIwthxMSyNsyNbfsCVXPp1rm4mLxM2yxlyZkBkTK9qTSeZXiGPFzfx9yWaqLjmD5NxF3L9sGV/dv4q0KG8zUi0W6bLi3NmAiijPizwX5C0vtUqp0SaEYhmmQwbOdbbO6lhRtl6pmOZXZ6OB6y/XwLS4blqv1bbbht2LF0hPFcpuoiV33TS35AbDUtmV+31gzQqi0w6msLyC2197g31KBvllMjKdNX9Sq2yDVFvDEm7DykglNSxFWvazHEvFhcZdFMvp2vIYU6J/Upn8x5KS10dIMq38u+AGtF7Eei/vclhq8bbX0QGioiiKoij9E02EaUPGWmwfTx8FyHhd085MpmeO9+xhcTZfcz+JWXM5amjPPf+wJ/CsxdsNrpl8HFoxkOZXZ7Hl8YeYUVbb/gZ9BGstGeuJ5d2z/7sHk3s0Smd+FEVRFEVRupB+O8PZGW5asYQ/LXiX4WVlXLvPVEoivaOftsd/XvC45No0Q2rX8NyvqqmtbD+wJR+3PdfMp363idpKh+f+N8qQqu77/uEzk0o5blgBRa5LdWH/ueze3rKeb7z7Kq5xuHLCwYwrqejtJu0UB1RUc+t+M0hbS4WzeyTZrko08PUPnqUh1cJlwydz7aoFNCRb+NmEfTiqelBvN6/3sZpSqyiKoihK36H/fPJvg2O8gGLXSlhKqLWGG1Yspe4LX2LTfXfxxta1HDOwJqDRSmTyqGPzp+C6MbFepGZKvHT+U+BEc+WtVPAyGa58Cs7/01TeuPVDHnhuE5+eGcc4QkMN7EDosknRH81NXHlfM6ddcSAfPLyUe15byxePjSBs1oAuG6b1Zppzeqorb8sUSbytH35HVYOXNrQm3zpCb/Rc0X6bX0W0IedC4gmtVSqfyZA0WjdEk5aYgCIa/CB/++oVlB5+PJlEgvsWL+GyAv8eSHmdSaVT6t2BxOQwhVeqsEJBzohjaEnExHJOqZVaaUDpFMvpjEupUwJAUqT3yhRZmdoq+zRt839BITVUmQ8VSJEV/RCWRhvGcxuW44yewMCR47nlqXuxe4+ncsIEbnjsCY4c4D+7Myk1WnEsAT3Yy59MK49Knm0TotEWBpTgYKJxbl+iJvFepf1LunPoTKWiKIqiKH0EVWoFRw2oYf21/8SuW8ukstLebk4oZ02x3PPdN1n6+kYO37vz3xmcPdXhsZ/MZslLq5kxQS+F9vDy6NaHl9Wy+YXHaHjtWQ4u3X2U1N2ZfcsG0vj+O6x//E6OHlBL4p132HLnnRxTXt0t+7PW4tnuGhl2E6rUKoqiKIrSR+i3M5yd4dujxnFe7RAGlziURvtu1/zgVIdzp1mq64qoKu38QPFbp8c5a3qECidJdal+0NwRb27ewNfmzcZ1HP488UDGlpQDMKNqCHsXV2KMoSpa3E4tSlcwpqiKa/Y+gSYvzd6VDhfU7UVjJs3woq6/N3hTupHfLH+AzakGPjXoCKaXje7yfShKX8YYcyLwB8AF/mmtvaLN60cB9wCLs6vutNb+pCfbqCiKovRt+u6oalcxEIlsr+rJ5FhJqzo7viCa1R59pTUSosJKghptTiWNiGUj02WFvmfcXP1OxMtbPtNQmNs2q6GOdsB1k9CUXR/PJZIGho4ytjWZ3K7tYwa7sAVIb6+Tmqhoj1B8jVBhXanaklv2hJ5pRfquzYgkWKnsukKjDUmsdd386mXY/WqplFBQRZ/vrMIJ8N81yyk69kS8xgbuXbCMy+L+bFrKc6mM+rPhSakIB9JNc8dTJK6ViEyjDUl/lSqwRKqzLalcGS9khsq20Wi31R9YFjpyIME1V0bWLyd7ZQKt3FdM9HXEzZ9G25GEVtmHBdEURTEHiJFMQ7ETodiB5qTow4A6K45d9G0qcE3IdNncgc1tWoxbM4y9Rk/jkdkPbxtwxgPnUSrguTbLvjLyGMVb0jNudrsu/sJH7+FUugBjjAv8BTgOqAdeNcbca62d16boc9baU3u8gYqiKMpuQf8dcPZDPGtxrMWY7v8w6XkWLD2yr92BoyoG8MKTj2Kt5eC9pvV2c3YrrLUhD0rp+4wvGsQdS19n65pFnFAxsbeb0yGsNfocTqWrOBBYYK1dBGCMuQU4A2g74FQURVGUUHTAuZtww9sJLr2/kWHVEZ75TJSBJd13z+Udr3tc9O8MtZUuT18KQyv1w+txNXVMLq0gmS6gJlbY/gYKACsTW7j8w8dpTif57sjD2a9scG83aaeoi1fyi1EfoTGToCZW0dvN6TA64FS6iCHAMvF7PXBQnnKHGGPeAlYA37TWzu2JximKoii7B/12wGmMxXW312EDqaRSoxO6pkyjDKSHCiUwGhXqbDynqoZqtAKZQOsW5JJj3UKhvAolLtNUwG9eaWbY98+l4f6XeGj+Si6a5kBEPA4lIk5lOp1/OSXq39K8bdEmxPFG4LfPOsz46SEse+JD7nl7BZfOCD52xSkQ9UjFMi3KpWTSqlALnVwZI/tc6KiEqLaeKGOkkmmFCioUUUfUEw1LCfbya7CeUDKbs1pruRMn4URIiKqa0/m13YBmLXXhkIGA3NbIhFWptspkWqELh9VpQtJupUbbkonmXR+mzkpVNSxGJyoU01mbFxEZN4Wa2mHc8/pLHFDmP7ZEHq/UcR0xFxoT71+p4yZEnwcSfQNastRoRUqt2G/Uya/4BrTkdJR4LEY1MVwj3tsBQ1bo07nVQU06kM8m/gZ119yvpyFgSpeQ749L24v2dWCEtbbBGHMycDcwdruKjPkc8DmA4cOHd3EzFUVRlL6MfirpItKe5anlW1m0efv7JLuCM0ZFWXPlXbTMX8ahI+QHaMvjczLMX5bawdY7x5mTPV76xUssn7WCI8aE3RNoeWpBmrmrdv5+yF3BWstLa7Yyd2NT+4W7mHe2ruf9xg09v98tm5izeVOP73dx83rmN67eLpl3Z9i3dBAN819n/bP3cUjJYF+v3YX6upOlzZt4c8tKvD7aPkXpBeqBYeL3ofizmNuw1m6x1jZklx8EosaY7SKjrbVXW2unWWun1dTUdGebFUVRlD5Gv53h7Gm+9Moqnm6E1IatPHpyHeMq4+1vtBP84OBizhsXZ/CQRgYU5waBX7sxzYPvGTatX8cjPx3A1LH5Q2Z2hv93vMOZ+1gqXIeakPTa7z+e4br5Li1bktx9vuXwUT1zKf35vbVctWwrXiLJX/YfytGDy3tkv3evXsS/1i7Gy2T46uApHFk1rP2NuoAHVq3gl0sXYYzhy3WjOH1Qz8wMvLZlKVetegUnEuWMsrGcOqBz9y9OKK7hqnGnkPDSOMbyqfn305RO8N0Rh7F/H9JrX91czy+XzcJ6GfYrreN/R87o7SbtEt05ZjbGXAOcCqyx1k7OrvsR8FlgbbbY97KDj7bb7jDxVOlzvAqMNcaMApYD5wEXyALGmEHAamutNcYciP9F9voeb6miKIrSZ+m3A05j7DaNUuqyUqOVymuYRiu3jQiN1o0JLTaaZvaGFgovOhVzz3PM35Rg/IDgwC8i1dkisSzWS6yQ86KVWwCYVAVuhZhBjRXxyodJZn5uBLPvW8PbC1qYOqrNpHVzTp1lc25fXrNQOKMiLTVb/dgqg0042OwhG3G8OPDSSsuw8/ZnwytLeXP1So4YB8h+E9qwTctUXtH/mfY/FUuN1lrDK5taiMw8FK9+FW+tX8uMgVWBNFpJNCJScwOKqFBnhW6ZFGm6SaG1JtIR3m7cQtGUA8k0NzJnxWYOKPG/UGgSmqtMN5VptBKppMp9WDfXJqmMzt7cRHzKARjX5fWFyzimsmC7Y5DXq0Sm96ZEPyZEm+WyTHD9oGk9xaMmEymt5P0FC7a1OxOi78pjd52g5loe8R9X8t/Vb+OMnUx17TDufP1lJpcM2b4eN7/amky725UFyMh0XLEsk3Xl4EuquZKnN3xI6dDxVI09gPnP3bmtfwvFNbSz90UGVVvxHkOq263ruhDb7fdwXgv8Gbi+zforrbW/CdtoJxJPlT6CtTZtjLkMeAT/S4JrrLVzjTGXZF//G/BR4AvGmDTQDJxn+6rGoCiKovQKqtR2ET8/oJz0P+9hSqaR44YX95g2+NOPxHj8j4upbElw9qEF7W8AXdK2n8y0rLvhZWpXreL8A/IPBjpDe8rlN8ZWU/zYc9QtWMy5I3tOyzq3ZiyROa9RvOADTqwa22Nq6McGjqJk3jsUzXmTcwbu1S37yHcsMyvHU1T/Id7cVzizsmvSWaeUDKZh/husf+5+Dioa1CV1dhUnDphAQ/18lj5zKwcX96227TzGv4ezsz/tYK19FuiMW74t8dRamwRaE0+VPoy19kFr7Thr7Whr7c+z6/6WHWxirf2ztXaStXZfa+3B1toXe7fFiqIoSl+j385w9jQnjyzh5JElAFz/3hb+33Nr2Ku6kAdPGkRFvOsGZG05br8oC/8YDTyHc0fcPtvj4us86qrgqUscBpd3bibk8NEOC74LpLtuJuWp+kYufmolpfEId88czsjS7bXkfauKmXX8eNLpnr10RxaWc82E42hIxnmvcS3fWvAwEcfh28OOY1hBVbftd2hhCddP8vVOGX7TVdS3bOKnSx4n5WX4xtCjGZ8dbA2IFfN/I08Cum62bEJxDX8aezIJL83geFmX1NlVTCip5a/jzqQhnWRYQd9qW6fonZTay4wxnwReA75hrd3Y5vWOJp4qiqIoitKP6LcDTmMs8ayu6ggt1g1RHaVe64iH1UsFNCrSWWXSrMR6hivnbqX2+59k3X+f4Jk1m/johCIiRS25bQvza7QBxTfaIpaFhipN3Rah1zpiZiIpAoS2Co220eGXTxr2+eFxrHlkHvfOWc7nD48EB42yDa7Nuz7wUHmh45p4rt9sSiS2ivRdT2irxshly5/eb6D4olNJLljGPYvr+co+A5HIQWZa1CM1TDdEmZRJuTLJVuq4ckAXSDwVGmxTJsL9GxcxYJ9jSSeaeGbVQi6sHeDvW+raooukQinV0Iw4Z4lA2m3+fctEVtfIfeVPlJVaqUyglctPbVxE4fhplBVX8PB7cxnbOrsXoiCnxXIg5VW0JyVmyVJConCwlDillDiQFFlTBSJBWB6v3K9MrA3o0aJvMyGzcxGZTC36UPZPgZumwI1SG48CucZ5gfrzVh9Atk1qxoFBe4gC3ZXs4uR7tTHmNfH71dbaq9vZ5q/AT/Ht4J8CvwU+3aZMRxJPFUVRFEXpZ/TbAWdvcuKwODf94TbcdIpph2wX1ternDbe8vtfPwHpDIdfumszZtbawGBnVzm1Ls7sGx/GeB6HzhjaZfV2NfsVDeaNOU8AlimDj+zt5uwSU4rreGL+MxhjOKlmem83R+kbrLPWTtuZDay1q1uXjTH/AO7PU6zdxFNFURRFUfofOuDsBn5xUAUXjU0ypMqjqrD7dNrO8P2ZDh+Z5FFVGqE2JIG2I9z3ToaP/8djYIXDk5c4DKvc9YHnZ/Yu56jBhcRsnEFF+YOA+gKHlO/F6MIaXAxV0dLebs4uMbmkjl+OOo209aiJ9QOVVPHnDHv4OZzGmMHW2pXZX88C3slTrN3EU0VRFEVR+h/9dsDpOB6xeDK7nF+RDUuplQmrUp11hPoX2Dagwvpl9q0IJtC6xTlF1ooPg0YqvhGps8r1bY8uS1p4iZsac8up7VNnAUzcr3PiCCAN22w2J8Rqi0iNVqyPWX77osPU7x3GmheWcOfcZXz1WBeb3zIGqV7GReqnUDvdrHI4rtaQanJo1RqTLbn7OFPJnE/suvJctP8s0ExaptHmBrNh9ycGkmzFcnE2tXRUpDUtNnc8JqBQin3L8y3KpIXKLDXUsARaqbB6Jr/yKlNkpTIqNV2piRpjqY4VZ7fNtTmsXyKiPXJIkwzRkeVlI8sXiPPXIlRpeYyFUdm3sp5cG6QeLJXmmFB8ZWqu7IdIJL8jK9N6ZYqx1HG9XbhHMritafPvrmMx3ZpSa4y5GTgKX72tBy4HjjLG7If/R+VD4PPZsnX4jz85OSzxtNsaqiiKoihKn6DfDjiV7uW0vT1+8bsXsWmPI7/SKwEliqKE0J0DTmvt+XlW/yuk7ArgZPH7g8B2z+dUFEVRFKX/ogNOpVN8Y6bDyRMtlSWdT7rtKC+s3sJnX1pMSSzGTdP3ZlhxYbfuT1F2ayzYDgQcKYqiKIqi9AT9d8BpLJGsUiu1RJlY68h007hIoI0JlS9EuwvUI1JnHaGMBhJfZdKsVECFZ2h29rarRH51NuAuhp3hSAfCIWXbYtuvnzjCBJ3JsG2jQmlOicRgoRwb0Z8yyRbgr4s3UHDW6bQsq+eBVfV8eeIQXFHehKR+BhJo08FE3HzLYbNCUu2MuUF9VyqdARUzpK5AQq4sL5RKqYy6Tn6tOZgcm6snE5IuG3gPhAWDiibLowy7LBNe/v2GpcVKXXZLKndBxcT6qJMkH1KFlWnCEfFeirr5U3Nl3xYIRV72TzKT/40i+0pqzIH1oh4nRKv2ArHF29fTQ4/tVRRFURRF6XH674BT6TccV1PC7PsehIzHwQft1dvNUZS+j+3Z0CBFURRFUZQwdMCp9HkuGl3NYTXFFDoR6ori7W/QDczbup7/XfgSEcflitGHMbKwfJfrXNy8nl8sewoDfHvYMYwsHLDrDe1HrEk28u2Fz7I11cL3RhzE1PLBvd2k3QQTmEVVFEVRFEXpTfrtgNM4lmirUhsR+qtImpWJqW6h1DuFuyh0v0DCbUykpEZDEmWTQm/M5GYcnCJR3pUeY0gqrFQpW8SyqDOg7HbgrIYZfAGtNx7yoTWsnbIeqbnGRP8j9OOCnD6ZaSrYthxJ5sqUVm0GYP8qyKQzILbPR7olp2pKpdYV5y4ttNbQNFahzkbIcPeGRRQcPhOvqYHHlizi0uFT/HYL1TMjO08cvkyLlQruI5sWUDLlcLAejy9ZwGcK/Ge2erJJon+TNnc8QR03hzx9UmEN1XQFrigjH0oj29wSogRnOjDAkWm0rkyFFssJ0VdPrK8nM3IsVcPHcOvLLzC5xH+Eo9Sa3ZCkaanRxiNCcxcEr4Pc+jAleGeRxyX7Jy4Sels1YCdEC+8s3RkapCiKoiiKsjP02wGnonQlh5YOYNYLT4C1TBt5QJfUOaVoMK/MeRZrYZ9Bh3RJnf2JScUDueODZ2ha9C7HVe/d283ZbbAaGqQoiqIoSh9CB5yK0gGGFRbjWkvEuNTGi7qkzgPLRjIiXgUGBsfKuqTO7mBlYjO/WPY4CS/FpYNnMq5oUI/sd1xxDX8YcxJNXooRXaAwK4qiKIqiKD1Pvx1wGscjUuT7pzI5NpBGK5TOQKKsUEADqmrO+myjwor1abFcJOp0xfpQn1XU6Ym0yxah5qaERhvPn3YbIGy9mAEJKLhxsYHXvjobSKMV9VhxkI5MxC3MVSSPS/a/I86Rl8gpsjK91qZzO0415h6TkhFloiJdNpOW+qSM3M3hSn26TdrrbWuXUnjkTDKNjdy3eClfHL6P/xqyXK6ujAhtkRqq1DUdYxkYax1I5dcvW0J0XFlGqpsFIem9Uj2NimRXN0TllPt6dtNCoqP2oaRsAI+8P5fRhYO3a4Mk0H6pfcsEV3GtN8q0WNE/6YhD3MSIuyBVaplA6wTU3Nx6qUSnA0nC8o3YPvJYwpKHw3RYqfVGnPxt3tYnXWzA2tA3vqIoiqIoSs/SbwecitKVHFpaxYvPPQnWMnXk1N5uTo8ysWgwjy18GmNcjqzsGp1Y6V6sp/dwKoqiKIrSN9AB527I7W9l+NwdaUYOdHns81BdsuMPl3e/4fGp6zyGVTs8/mXDwLJd/zD68ByPC//pUVvp11lX0bk6n3jP47z/eFQVezz8qQgjKvvmzMyJA4cxsaSSdCbG4IKSXmnD8sQG/rzyUTzrccmg4xleUNMj+51YUsfPRpxGymaoimiSbt9HU2oVRVEURek79NsBpxPJEK/ZBIApzJ9SGUiXlWqoNC7Lxf16jiiUFHWmhdoqVD4ibv5lWV7SmPNxvcbcvmwyd5pMQZr/ex6m/+holt41j/vmrOFTh7jBJFtRpQF+8bjDoT84mA8fWshds9fw+RlttMKo+HCa7kBapjH84gmHaf9vOitfqOe211bw1eODdQb0Y4lQjk2R5dezDPt+eRpr31rDf+cv5dsnu9i0UKBFYq3UiTMNOY1WPnPQCn3SE6moMrVTqp2O1Ghl6qpQL1PZemqjlaRcl4zoa6nIyjTasDTXDhjOAZ1VKqZPbX6PkpFTcSJRnl4+nwvjA4FwLVYiddN8KakQTJH12jieNbFSABKeVFhzy7KdCS9/X0s9tVEo0VJFrhA6e4lQooO2uUh8dXLHIjVaeV7CElszIbOAUq3OZPIruLLOlLj+ZD/L/glqtLl6WtODTahn3zm6tjZFURRFUZTO0zenk5QdcsJYeOvXL7DunTUcNLL9U3jCeI/XfvsqK2ev4ZDRXTPzceJ4yxt/nE39iys4bGzn6zxpvMdbf32d+meWcvg4nZXZEeMKhrBu0cus+eB5JhTU9XZzFEVRFEVRFKVd+u0MZ1/n5hfTfPG6NGOHuDz6jQgVxR0fbP3fSYZzpngMqnSoK29/ux+fYjhrnwwDSx2GVHbNoO67JzucPNmjqthhWFXn6/zqTIeZ4z3KCh1GDNi5ep7/MMW5dzZTEmvk/jMrGVXRvy/nKcWjGBStxGIZHK/odD31LVv43uIXSGUy/HT0Qexd0vOa7Kb0Fm5Yez9NXjMfqzqBkQVDe7wNu8KWdAuXL3mKdYmtfHHIIRxUPry3m5TD6j2ciqIoiqL0HfrvJ3TX4pT5OmZA74yLQ47F8i+7Id0ilVpZJpXMX8YTomSLKAP8/CHL6b+azuzr3+eB17Zy4SEuXrPQABNRsS9RjwcGw/5DDDhgsxahkYmyIhXWfx6fYb+6bPlsVSYuU3ZDBLyg+yeW/fX77rWDxM+USKMVhx7Yb8zffsrYXJ3bHUsyp0naLUJRLEjy2zfS1Fx0OM2L13HLe4v4/oxiXNFvRiiWkWhOvXRTuTJStpbJsimhl0rVNOkFr43ga1LJza/R5k0oJVyjTXjBlNeB8fLttg3DIX9K7aMbFmMn70e8tIz/vj2X/y2q9tts88+WSzU0KpZlILNUgqNiX1JbbRHLs7e8T2HZCGqL63hx3dvbBpwyzTWgpIpjKYiIcyn11w5otMF+y5WRx+4F9GtRXGz68uZlNA+ooXbiKdz+wsN5B5yyzVKZDbsGugoL2JBzqSiKoiiK0tP03wFnH+f4iYZbfvYmqcYU08/YuUc1KD4njoIf/mcWXtrjiDO75tmYewL7ldTwwFuvYozDtMETe6UNQ+J1vLrxQdZv/oAjS/bvlTbsCmOLa2hc/CbN61ZwbOmI3m7OdoQNuBVFURRFUXoaHXD2EtNHWP75RIrRg90uSY3tKzw2J8OFV3vUlBt+cKLlS7dATZnhkS8ahnaRztvKFw6OcOTIDAWpYkZW6qC9o0yvGMxVBUeRsh7DC8rb36AbGBobwicGnEvSJhlZuPsl3w4vqOTKMaeyKdXMqMKq3m7O9mhqkKIoiqIofYT+O+B0DaY8e3gFQpeN5/xaW1KaW47F81ZjWprzLksF1xYV5y/T3JRbFnqtbfT42QNw+C+OYcGNc7jvuc1csH8UmxJptBGhkqbEYEomgMoy8kymRJm4SGEVZWxCfCKVPqTAxKSmKxQ92Z8RUWlTC1c8ZjngK/uz8pWV/PjhVUz+8nTWvraC296q5xunuBATqrBM7pUqclrImkKpdQpzDfWyQufkkZDZbGkVPKVG64r+ccR6qTGmpcoaotEmMrnlprRoP0EVViK1ybAUWZnamgqk3ebXIZ2w5ZA0VIlMgm1OR6mMVGWXRXtC9iuTV1ts7njl1wcFQm1NieNKCrUzLa7d8phHecx/vEyp0J3LRTJtgUjTLYrlvGyZLOx1SJ0VhOiyxuZXbQObttlXVbSIqmhRtn/87aW6LHXfwHrRn92h1CqKoiiKovQl9EafXuLYcfD6L19gw3vrmT60/8zOnbC35Y2/vsmyF1Zw7BiPd/72GiteWMah4/RSU5SewWBt538URVEURVG6kv47w9lHuPn5FJdek2DCUJeHvupQXuR/oLvyLPj4AWkGu3EGl+1+g7FH30zziataGFzl8tD3ihlc5R/D/zslwvGTPSpdy4gBhs8c5lFZGWFEdd/6IPvWpi188c35xJ0of9p7f4aLWWoFViW28LOlT9PipfjGkCMZVzSwt5vU62xJt/DjpU+zLtHAF+sOZnr5sN5uUn40pVZRFEVRlD5Erww4jTEVwD+Byfgu2qeB94BbgZHAh8A51tqN2fLfBT4DZIAvW2sf6cBOcupnadm21V5Z5bblTEnFtmUbyWmiTjKnxTpObvbRFhSK8jm10pEarVz2LD+5J81JVxzEG/+ez4OztnDeNJf0Bn9ws18R2IxLJmveGqHg2UzIIFQm3wpNz3hCGRXqqRHV2BaxnBKJnmmhSToyEVfU6Qr1LxbjiocSnPT/xvH+c+u59cUmvnp64bak2f1GuZDtz/2mENRlpYIblvSbFJ5nTGq3oj1peYxCKRV6ptRopWrqeQ7/XLoac9RMWjZt5O5ly7ls1ATSnkyKzbVtayp3bTSmgkptVPSXXI6JtkpSQjFtETpuJmRmKUzH7Yiy6wk1VKrAMj1VlpHrn9i4EDtmEiVlA7hn3ly+UjB4u325on9lsm46cFy59pREc+ULRPJyqUidLYslti1XFuaU9LBk1zCFWOrB0hh3xW9WOLWy+2WbpWn78ualNA6ooXrCidw+61EOrhji1ymuv6g475HAtZFf6W5tf1cPD22X16goiqIoitI5emtq7Q/Aw9baCcC+wLvAd4AnrLVjgSeyv2OMmQicB0wCTgSuMsb0OQfV8yyz3mli+frgQOOYiQ5P/+IN1n6wmanDu767rbW8vCTD0o09ey/YzIkuD1+5kPnPr+eQCT37vcUbSzw+WLNrx3t4RRktzzxFy+uvM7W8Y6EvS1s2sCq5aZf22xlWJTayIrGhR/c5saiWhvdeZ9PsJ5lSUNuj+16V2MrCpvU9us+OMLaomsYVi1j//H3s24dnfP3HoqhSqyiKoihK36DHZziNMWXAkcDFANbaJJA0xpwBHJUtdh3wNPBt4AzgFmttAlhsjFkAHAjM6tGGt8Nlf1zDQ6830bQpzYu/qmRMnd+1f/pElE8u9BhqXJ5b4HHwlTCmppkHzyugvKDjH+7unpfkkgdbGFkd4eELYlQW+oPXbz+c4YZ3XVINCZ66xGVKXc98h+BZi5ed/rE9ONb94+MeP3vYkE5m+O9FhmMmdO54PzZ0MPuWl5JOFjOssKTd8s9s/ICb17+J9TwurjmcfUpGbldmSfN6rlzxFI4xfGfoUQwtqOhU2yRvbF3ILetngYGPVR7EtLKxu1xnR9i3dAj/N/JEkl6aQbGaHtknwLyGNfxs6fOYaJQLkyO4cPCYHtt3e4wsrOLK0SezKd3MmKI+mEwr0IGjoiiKoih9hd5QavcC1gL/NsbsC8wGvgLUWmtXAlhrVxpjWqcQhgAvie3rs+u2wxjzOeBzAMNrnG2JtF5JTqnNlOUewZApzc3c2EgupdY2rs2VKco9NkJqq07Tllz5ZIKn32zi3O+P4pG/1/PGwpQ/4GxM4AAH1UGqvoTLH22m7lunsOKOl3ho3lbOmZRLzPUrDf+Q+ONZGeq+fgprHnid+99dxycOiGNiGZ5cAuMuPYyVD8xl9ro17DvR+OJxa9ukRpsQerBIxPUSQU20FReR0FsgKm1o4ql30pz01TF88Pw6Xp63lYNHWSjIn/RLVKTaSr02IRrXklMpA0iF2IMn3jPs9+mJrP9gEy8uXMYx4wAnJGZXkBGaZ2uy6ZiSUhqbc8/vlBqmTJ9tybjMaVpD9bjDSSca+WDdKvbLPntR6pGPbXmX+L6HYFMJnqj/gE8XTctb17b9dUCj/aBlFVVjDsQ4LgtWrOBgM2a7MrINMrW1JSRBVxKWdutZQ4VbBS4YI1VhkVgr6k9IhVV8AVEcya8cx4WOWxrN6dQfNqykYMJEYsNHMfulWXyiVTkN04bF8cpBVth6uRysMyztNrcccT0GFRYyiMKsftyaTJs7LqnRxkTKbkc0YEVRFEVRlP5Ibyi1EeAA4K/W2v2BRrL6bAj5Pgnm/cRmrb3aWjvNWjuturxnD+37n6zmmm9+QEUqxYlT8w+8jh7hsPqqR2hcuJr9Bu3cWH/mMJdVf3uchvdWMnVIbtvLj/aY/+snqVy/ljMP6Llj/t5JDo/+9n1aPtjEuYflH7B2B9+aaZn3rzk0v1rPJw7queM9rmIiW957keTSdziifHzeMpMLB7L5refZMvcVJnWRcjmjbG+aF71B44JXObps7y6psy9z7IBhlCxeTMvjD3JR7cjebs7uiTVYr/M/iqIoiqIoXUlvzHDWA/XW2pezv9+OP+BcbYwZnJ3dHAysEeVlHORQYEVXNeaG/y7kf3/6BvtOqeLmfx9HUdH2XXLzPfV8++dzmTK+hNuumkZJ8fZlzj+2nPOPLcfZFH6v3R+Oj/GJ5RnqCsupK92521B/dXQB54xPUzfAZVibwbTBZme8Ov9h8ZEPknzq3gR1FS4PnlvAwJIdD+aOneKy8k8uxDp2CT39doILr9xCdbnLgz+sZEh1527DPXycQ/2vHWxjz84SjSio5ld7nbNDVfHIitGMKqjCwTBczIzvCoPjVfxk+LnAnjEzVhMv4rqJRwMQcfMHL+0KG1MtfHPBS6xLNPG9kVOZXjGoy/fRF9DQIEVRFEVR+go9PuC01q4yxiwzxoy31r4HzATmZX8uAq7I/ntPdpN7gZuMMb8D6oCxwCvt7sg4EPNVTpkoK9NovVgxv/rjO9z6j4F8/4pNPPXCak4+YQSZMjG+tWmuuOpprvpLGVde2cRjL23ljBOHYCJCN03nHlbP1q25+jfLiFiDYwwHDnXIJKK5+x49qfsJ7U5+2Hb8j4/Th0Vwi1pozd10Kiw/exZOuHw/5ty1hLvf2MynZ7hYYafKtFuZRis12kxznJ+8mKD8cyey7oV53PH2aj4/rQgjytiNuXoccbymWOisEdlmJ7B8xd1NHP+lkSyavZmbH93EN8+IB9NoJWlRTyrXJ1YE2Yal7HpCFc6khTYslE85aJQabVosZ8R5KRDnou2AU6bRuo5lZFEFsAON1ss/kA8bTMq9SXUzLJk2FXJsHRmqyjrlMcv18rgSIcpuUST/tlLHLY7mrqFCsZwRKb6OzZUP+/ojE3JevZAB19Pr69k8uI7S8ZO57rmnmFY+eLsyrsmfLivbZkSPhqXUSo1Wnjt5rsOuh11lT7iH0xizBf8tYoEioJncpV5sre1z4XKKoiiKsifSW8/h/BJwozEmBiwCPoX/mfI2Y8xngKXAxwCstXONMbfhD0jTwKXW2i6b+jj80MF88TvL2bjJY/LE/EEgRxw6mO/971I2b/bYZ2JFV+26SzhmPNz027m0NKSYdkLnP1/NHOJy9XVPkmlOMv2c9kN0dpZjJkb4/VVLSbZk+Pk3CtrfQFG6gb1Lqmj+YBbJ5Us5qmJobzen++j/k+FYa7fdnG+Med1ae4D8vXdapSiKoihKW3plwGmtfROYluelmSHlfw78vDva8offzODV2WsYOaKMQbVFecv87pdH8trraxlZnWZwbWHeMl3FPe+1cNljjew1IMJ9Hy2iomDHMyC//IjhI/t7DK6IMHxA52c1fnhoEaeMSjGwtIARFV0/MfCtMwuYOSVKRSzN6EG99TSezvNh8zp+v+IZIsblG3XHMCi+68rsisQGrlr1OAb4wqBjqYv37eTT/sCEkir+NfEoNqUSjCka0P4GynYYY64BTgXWWGsnZ9f9GjgNSAILgU9Zazfl2fZDYCt+tFnaWpvv/4HOtitirW3VJnruxnJFURRFUXZIb81w9gjW8QdORiSdmnTOy3SaNuAAh06O4ZWKD/uumIFLbsJ1HQ6aXovbsBaySbVOS2Ouzs0bc+W35vRAmxDJt0L7tGmp2kpNL8OPX04w6Gsns/ze2dw/fysXTCnEjYg2FwuvNOH7ZAcNzQ40E9lpDWmqpoWyK5Npk+LUZ5XdaUOjgUTSTGOuHyKlOYXYJkT700KpFdqgEdXbjb7je0Bxdv3m7AtRMUDO5Fdn5bEE9GCpBLfE8y57KaHaim3TQrVN5UmvhWCiqovlkc3zie9zGJnmRp5dNZ+LBk+lLVLDTcnUVlFG6pRPbZ5H4ZipYC3PrHyXC2oO266MVFKjIWm8iTBdWCwnRZmUaGeh0F/jIRptS4hGK/srHqL7ymWp0cY6cH+mF9KHUpcNS6YNIwMMiJYxINr6vEp/vUyjDetnV8Q/B6+V/BqtJOy8tCq1XTkhaQHPduuXOtcCfwauF+seA75rrU0bY34JfBf/sVb5ONpau66L2/QscLsx5iHgCHwjRhEYY04FHrTWth/rrSiKoihdyO431dTPmTEkwqq/P0HjB6s5YLB+Sd9XmFxYzea3n2Pr+68xsahrnks5vmAQGz94mQ0LXmZcQf8Mr1F6A4O1nf9pD2vts8CGNuseFbOLL+GHu/Uk3wIeAqYAr+HnAChBzgM+MMb8yhjT/yOvFUVRlD5Dv57h3B357VHFnLs8xZDyQoaVa+ZFX2Fm1VhGFw4gYhyGFlR0SZ3Ty8Zs02gN8P2l/wXg0kHHMKSL9dq1yU38c80jpGyGT1Yfz5B41wyalT6I7fXQoE8Dt4a8ZoFHjT8V/Hdr7dWd3YkxphK4HDgyu+p54AfW2o3hW+25WGs/bowpA87Hfw62Bf4N3Gyt3Rq2nTHmROAPgAv801p7RUi56fhfNpxrrb29yw9AURRF2W3pvwNOazHJbFxrMhfbKpVaIvmfl0mqYduik8yps27j+tzyulW58ptEMm2j0ACl9imWvaRIfxXaoMkqwNOr47hFuTY7caHppuSktNRZ5WqhHIYkuBqRrIlQjr1kLsXXT8Rtbaf4ANssjiVkX1aol7IeqQQbL0QkTIo6M0KZbM61Teq+6YbcfbXJ5tw5bWkW68VxJdOy/bmOk+mk+ZTPCSVlWVU2V64lk+vTdJhGK5a9Nrrp8MIKAG5Y8yLx0fuDMTy94l0uqj0EgIgoLzVOqd1GxbLISw6UeWHru8Rr96YkXszzq+fwiaKjACgSxylV0lRIem9Y6qxcL/dbGMk50VI9DUNqyRnyf+ESps7KR4GYDkiqMl22I4+ckW2T20rFV76tgqm5OaR2220Dw11zdKuNMa+J36/u6MDQGPO/+CL8jSFFDrPWrjDGDAQeM8bMz86YdoZ/489mnp39/RPANcBZnayv32Ot3WKMuQMoBL6K31ffMsb80Vr7p7bljTEu8BfgOPxHlL1qjLnXWjsvT7lfAo908yEoiqIouyH9d8CpKLsR4wpqefWDl8EYxg/oshyVbYyKD+LVFc9hjMP0ygPa30DZk1nXmTAfY8xF+GFCM618xpPAWrsi++8aY8xdwIH49192hlHW2jPF7z81xrzVybr6PcaY0/ET4UcD/wEOzJ6HIuBdYLsBJ/75WWCtXZSt4xbgDLa/R/ZLwB3A9G5qvqIoirIbowPOPsz977dw6WONjB7ocN8FUSoK+/6z9Z5ZnOITdycZWOJy3zmFDCrR24Q7woFlo6mLVQAworDr02onFY/iC9FyMjbDyMLqLq9/faqB3654guZMki8NPpIxRQO7fB+tbEg18/3FL7Ip1cK3h09j39LabtvX7ogfGtSzfyuy2uW3gRnW2qaQMsWAY63dml0+HvjJLuy20RhzlLX26Wz9RwONO95kj+ajwJVtZ5SttU3GmE+HbDMEWCZ+rwcOkgWMMUPwZ0qPQQeciqIoSh7674DT86A5+7mnpCy3Opb/sSZGaLStSbQA7tbVueUNuWW2btm2aLcKva4xV39aap/NueWA5irSU6PFuSRYx83w41cS1H31eOrve5N752zk4/vFg+qfk9MVjZD2pKYnk2klXiZ/aq5bkMhXPFCPnLrIJITm2hTnJ89uIHb+cax5eyG3vLGcLx9YghNPinpEoqo0LEWarlSRkWpnU64PM005dTYp+jwlUmpTQqNtEYnB8sO41BtjIrHVFbpvQqbaekHNUyaOpkLUU6nROiEDAddYRmUHmk6IRhum6Qb0WlmpUF4HxDMMiPvPV407QjEX24YpwQVu/hRW2YYXN7xHeuhoSiprefD9OXy3rHK78hKpLweO0ebXaOU1/eS6ZWwdPJTi4WO4YfZLTCquy7tNvjbL/co5OKnFhiE1WklGpgSLfQX7Kr9G27pf24H97wzdeQ+nMeZm4Ch89bYe/z7K7wJxfE0W4CVr7SXGmDr8+/5OBmqBu7KvR4CbrLUP70JTPgtcb4xp/QZlA75Wq+RnZdvBpjHml9bab1trnwjZJt+F1PaN8Hvg29bajDHh150x5nPA5wCGDx/e4UYriqIouz/9d8DZDzhiiMPt/3iG1NYW9j8k/zNC+xpHDory1/8+Q7o5wbQzy9rfQOkXjCus4aEPX6Bp2bvMrBzfvfsqHkDThy+QWPEhR5aN6NZ97a509QA2ULe15+dZ/a+QsiuAk7PLi4B9u7Adc4GpxpgSwOwo+EYB/Psw2z6q5qQ86yT1wDDx+1BgRZsy04BbsoPNauBkY0zaWnu3LJS9D/hqgGnTpnXlk4AURVGUPk6/HXAuWOXRkrQUxHpXQ73rvWa++nQjYyvj/PeEAZTGOq6Y/m5mIecsSzO0qogRle0n1j401+Ozt8GIast9FztUFvX8sX9nWgnHDk1SXV7ImKp+e3kpbZhSOoSfxI6nOZNmYkllt+5rYslArhw9k62ZJOOKBnTrvnZPOvZ4k90dY8zlbX4HwFr7415pUB/FGPMF4IvAaGPM2+KlUuCFdjZ/FRhrjBkFLMd/tMoFsoC1dpTY17XA/W0Hm4qiKMqeTb8dETRnDM8sjHLCtAIyZbl74mw0/0yhSeZuO5IabWRNfa7M+rW5etbnkle9hpyuKRNT080F/PClzRR/8SwW3vMCD3+Q5KzRZaRFSm1cJLhKWlNtD6qN4cZT2Gz8qBXapxEaKsbww8ccJnz1YJY9+D53zt7Apw+KBNJlMyLlVeJEc2quLC+TbKUGLBN3M0JhdaJpDHDg0ChugQdk2yqVQ6nLSnVUrJf6bkCjFf2W2Jo7j2mh9SZEexIyDTgk4TUidFFXPA+92cvVKVXZ/FEo2e07kHQqk2DD0krDNFqJLOOGpMVWRHMqc9zNneOESNZtEom9sj1FIlFWqqRSSXXaJO6Oz763MlYeY67NUivN2Pa/eAkor20GUDWxCmoAD5s3kTWwX5kca2Q/y5212xwyYtZQziCG3S8Z1leybbHseelIqq6yHXJGsxg4HXi9l9rSl7kJ/xmlvwC+I9ZvtdZuyL+Jj7U2bYy5DD991gWusdbONcZckn39b93UZkVRFKUf0W8HnF7aY+KI3j+8wwbFeODah0lvaWbypCHduq8jRlpu/ftrtGxJcMBB0fY3UBSl/2F3/MVIf8Fa+zv5uzHml8B9vdScvoy11n5ojLm07QvGmKoODDofBB5ssy7vQNNae/GuNFRRFEXpn/T+iKybmDg8yrCa3j+8PxxewfkrW6iLVfP2+hZOfWQlo8sLuOmIOkqi7WuyO8NvTjWcPSlFbWGMhes8hl2RZERFhq9Nc/jKo2mGlSW46/Qyqgp3PTn22aUJPvVIA7XFEe48cQA1RV17LMquszKxlZ8te5605/HDEQeyV1H3qq5K38DSvaFBfRVrbdIYs9YY41pr23/o657DTfiPq5mNf3nIi8MCe/VGoxRFUZQ9h94fkXUT0YIomepBAHgFxdvWW0cMjNK5tM7I5lXblmUardRo2Sg02s1CudxUkqtSKKDpFr/MQQPipJoLOPvNNUQu/igLHnmWx1ds5iOjywMfDKUyKjERoSgKtdUGlFd/+dA6wLFccLvHkC/MYNXj7/HtZ9ZRcPGJLHt2Dne/t5FP71uCkRqt0BuNTPEU6bWe0Fy9ZJSfvLKVyPnHsfKND7hj4Wq+eEBpQM0N6LgZJ/+yVFXFvqSmm5TqrOgfmUYrNdqkSKYN+9DtiGOMubl2NiVz9WSEWryjD+9y6G5DE1lt3mVJOkSjtSGJurKMHOqXCo323tXzaZk4EbewiDvmL+B7pVMBSIiP4nEnfwKt1GijokzEyS/5yoRbY/P3nUyCTYvrQCrLARU2pK+Cam7+/gnrZ5ko2xFkm8MU37DrI5CALNTtwmgqtz6S3q5sV7AnDDiNMSOAPwCH4g+iZgGX6WAziLX21Oy/o9orqyiKoijdgT4ksQc5uLqQxG0PkFq2iikDCtrfYBc4Yhgs+/csNs1dySGDHLbe8jTN79Wzf23++zh3lsNrIzTe/gzNcxZzQBfVqXQtU0qqaJnzOk2vzWIfnd3co7CYTv/sRlwD3A4MBgYBt2XXKQJjzAE7+unt9imKoij9nw7NcBpjpgFHAHVAM/AO8Hh7934oQX69/2A+uraBEZUDGFHavYO0X53ocsaEDDW2gr0qXV5YlmJQwQBGV3bNpPYPDizluKEJakqKGVvVNfeLvlSf4hMPNFFZ4HLnKRUMLlFNd1c4omoIdfFi0tYyprjzaa5rEo1cvuRFmjNpfjhyOuOKq9rfqAvYlGrm/5Y9w5Z0C18dcggTimt7ZL/KbsMAa+0N4vcbjDHf7LXW9F1+u4PXLHBMTzVEURRF2TPZ4ejDGHMx8GVgMf79H+8BBcDhwLeNMe8AP7DWLu3mdu401o2SrvA/oHqFuQ/Ixstpn5H1S7Ytu+tW5sqsXZOrZ31Of8tsEbrslpymKzXaVLNIrBUKaCSeJAIcOTyOtWabnifLuBGRpOrmV1tlgqtMrHXiuePymuMY4PAh4KUAMhw52sGmPcBXLqVG6wjdT6bUekJP9UQb3AK/jsNHO9kI0NQO67FJ0eaMm3fZSzv87KUWnLOPZM0HK7j+9dV8bf8Bgf5MtOT6OZkQ/SwTdAOJsrk2RCJCIU7l+rxZpLSmPVGP6B+vg7M+UouU6oDUUKWWmZL7kGq1TNQV9UhtVdZZIPpdptEmMy7DCvyBZovoI6mbyn6RqapSqb19zfs0jBlLpHIAt7wzh5+VTcu2J1enbI9spzwWqSmnQ1JqZf88vXERDYOGUDR4JLfOmc0PirKKvDgfEZNf8ZX1ZDqQKCuR5162OajX5ld5A9ecPEein+OR3N+UgqwCHaYPd5Y9QakF1mb/j2oddH4CWBtefM/EWnt0b7dBURRF2bNpb7qrGDjMWtuc70VjzH7AWKDPDTiV3Y/DBrq8ftcsMi0pph5Z09vNUbJMKqniznffwolE2a96ZI/td0xhNXctfZaWlR9ySKneftZRLG0e+dJ/+RT+PZy/xL+H84XsOkVgjDnGWvukMebsfK9ba+/s6TYpiqIoexY7HHBaa//SzutvdmlrupHb7lrE5f/3GvvvU8W//3QEBQWqa/Y1vjm1hCPrkhR7hYyvjLe/wR7G6uRmrlr1JB6WywbPYEi8Z+7LPLhyMH+KF9GSyTCxtGd0WoCJJYP42YjjacgkGVfUf76AaEqn+NaC16lvbuAHo8dxZPXArt2BNf5PP8daWw98pLfbsRswA3gSOC3PaxbQAaeiKIrSrXT0Hs5RwJeAkXIba+3p3dOsLsBxt6m0NhLnJ796nT/+upSf/WoDzz4+n5NmVAfTaFfnlr11ORUuIxJoU1KjbRG6qUxwFZqoK5TXjFAaEZqefCq9MUIxFXUaoUy6hUmxXqiasrxMi/Xyf/A0hKTRJvPfW+rGcxqgTKOV7ZfHtSN1Nreca3Nrf+5fVky6JUYqO6cuNdqW5txyRtSZFsuO0Bgjot9SQp1NiOWWVO54k6KehGhbIBW1jV4rdcoo+VNepZaZCuiaUtvNj1RSn948D0ZNJOJGeWLFO1wy+BAgmICa9PK323WkLiuup8D1J1RbUcZaw15F5duWt20rdFbPinMcogenhIIbUEhDxkaeNQwpqBDldzxtZ0P264UotQHt1ubXgNMdSKOV5y4aSMrNf1af37iCpWXFxE88lj88+CBHDKht79B2mo6ow7s7xpg7gG9ZaxcZY/4KHAb81Fr7315uWp/CWnt59l+d/VUURVF6hY4myNwN/Av/odphn437NNOnDuTyn69m5aoME8cUt7+Bogg2ppqYtXkJ65MJKqKFHFQ2gspoUfsbdiGjC2p4aeFrYAzjB0zu0X0rXce4knKSc+fjPfwwh5SUdcs+drO02c4yNjvYnA6MBk4AHgV0wJkHY8wA4HL8DAYLPA/8xFq7vlcbpiiKovR7OjrgbLHW/rFbW9LN/P1PM3nuxRVMrNrCsLrufSSJ5KElW/nmq+sYW1HAfw6royiqT6LpbuZt3sLX5r1HUSTC36ZMYHBh58+3Zy03rXqD+9fOIyOmoW5c+SonV0/iY7VTccz2H+7rWzbx6/rncYzDN+qOYHB81wcWB5WNZlCsHM96TC7Vx5z0dZoySX69/DlWJ7fwlaH7Mb28DoDRxWVcO+VQVieaObCqvJdb2S84FbjNWrvSGJNut/Seyy3As+Q05AuBW4Fje61FiqIoyh5BRwecfzDGXI7/7XGidaW19vVuaVUXE12/iChwwmSIrFkL2RBas3rVtjLeqtxgIrm+Ire8OTcbmhGJshmhsLpCMY3EcuppsjnO5W9txFx0Ju8+9ByPLm3gjJHBgYLxhL6bzn9faaSoJVdeqJHyPi0jlFeZCmsi+Z+BLhXcgEYrNEATyS1LjVYqvrINmVR+zdhmhK4o1sv+lGm9SdGelFifEpqrLBPQWiNp/l6/nOYZh9O4dh23LVvFZWP2Iin2mxLqbDJkuTVF9eZVs7l37VzaksFy37p3cIzlgsFTgaDCes+G97Dj9yedTvHQyve5qPZAvy8CqmcOG6JuygTamJNhYok/QInKVFjRvwF1VijXUruVqqfUjmU/hqX9OoE0WpkOnL/9Uu2MOiHXYoj+KbVbE5LiagP6q2xzroxUaoOKb658WLKu14HzJc9FVPTnK1uWsaakgLJJM7j2pUe3DTgTqSg1kQpqIhVYmyRtu35Gcg+Z4XzcGDMbqAH2M8aUAZt7uU19mSpr7U/F7z8zxpzZW41RFEVR9hw6Ot02BfgscAX+M71+C/ymuxq1O2KtZd6GFja0BD9UTxtQSOL2R0kuX8PelYXdst93V2dY29DzsZTvrc2wamvPG9ZLGptYk2gJfX1qWQmp514k9fZcppR3fmZxY6qJB8Rgc/r06Xz/+99n+vTp29bdv3Yem1LbhziPjw9g6/xX2frBG4wtqO50G3qTtYlmljc39HYzeowt6SZWJjZ2WX0jCippXrucja88yt5FPRe2ZPEH3J392V2w1n4dP5V2X2vtBmvtFmvtUb3crL7MU8aY84wxTvbnHOCB3m6UoiiK0v/p6AznWcBe1tpkuyX3UL732npuWdKEm0rz9BlDGF7qz8xdOb2WF1c1MqxoICNLc8mrj9Vv5ptvrGJseQHXH1pHcdSfLXq8fgvfmL2Gvcpj3DRjECXRHafp/uSpNH95zcOkMzzzRZe9a3tG2f3diwmueCkNaY9HLixi/7qOXkq7xm31K/jtoiXgeXx95Gj+taKekkiEP02cwsC4r85ePLyOfctKiJso40tLO72vlzcv2abRTp8+nVmzZuG6Lj/60Y845JBDePXVV8lgmbX5Q06q3juw7TFVYxleUIHBMKKgi1NIe4DXN6/lfxf494t+acgkTqwZ0dtN6laWJzbw6/oHwXE5tnwiJ1Xut8t1jigcwM9GnsS6VAOHVFXscn07w57wHE5jzIzsYqUxcobdPmOMmWqtnd07LetbGGO24n8PYYCvk3tuqQM04N/XqSiKoijdRkdHCW8BFWyTUXcDMincrb4yG1mzbNtqs3LFtmVvRW52LrFqwLblpvW5+6pSzblBotQMY7Hc2Nu6Dg/XN1P59XNpuvlxXqlPM3iUr+JGHMORdSXZBFd/f9Y6/HjeOvj4mbz72As8sqyBs0ZVYtOGH7+zEXv+6bz31Es8tHQL54wN3uNlpXZbkOS+BZb9//colt41lxc/WMeEykhAYQ1sK3XWRE5VDWi0IrU0oNG2UXnvWwQjv3QMm2Yt4rnFy9lvYDyo6QaSe52Q9TK9VrRNLFsbVDuf2LiZ4hNPIb1iBf+ZP4/kYUewds1q7lu9hC+OGbVNu51SOoBEMkZLVgCXabQypTYV0EKD/bYpnZu5POGEE3Bdv6zrupxwwgm8+uqrAGzNNBFzM0El13MYWVCb3Ud+XTOMwkiu34sjIh1YaKWJTK6PIlKRdfJrtFE3V2dM1O+EaLSvb1lD0X4HEqkcwItvzubUQUP948rk768whVPqppJMiKoqCSq1Ylsvv0abCdGSZf8nxXJCLL/TsIbS2rFUjtiPt956ghmlU7drm6wzJnRleeSyzVHHY1hhCcMKS8CmyWRfSue55rp2YtHsESm1wDfyrDPAM8AnAB1wAtbazn/rpiiKoihdQEenw2qB+caYR4wx97b+dGfDdje+tnc5K398HdXrN3LUkJJ2y0+rKiR516Mk61exd2VBcP3dj5NctpJJVe0/i/JbB8Os7z2Gs2gNp07suWeLfmOqw4JfPwpvfchZE3rumZkXDx5Ew/334M55ixmVlSRnvUBq/jwm74I6G0ZFJKdAP/LII2Qy/gAuk8nwyCOPbHutMtr1qnRvc3z1MLy3Z7P1yQc5p3p4bzen25lSPIrMunoWvXQzRxVP6u3mKB3AWnt62x/gsuxrX+3d1vVNjDGVxpgDzf9v77zj6zrK/P28t6o3N9mWbbmXVCdObw4QUggJsJRQA8sSwsKy7C78aAtkKbuh7lIWslkIfYEASQgQSggpkB4ndmLHdtwd925JVrltfn/cI933yOdYklWudP0++dyPR6M5M++ZM1e5c+c73xG5uPtV7JgMwzCM0qe/K5xjTnKz6cUOUqkcicTwSUx/9+JhPrJ8L4saknz/okm8ZW4tLte/Sd+Xz5jMa3a3Ma1qIjOV1PYLp0/mVXtamTmullm1wWdiat5wWpzXnxpDApxSh5OrFyTY/5E4ZEdGStvNueMaeGzpReRyUZ5vaeGevbupisdYUN33JH+gnFM7gx/tzMtmn3zySc477zwuv/xy/vCHP/SsbkYRzq8rvQlZc0U1dy3Om1eO9NgqBrWxSj7e9Aacc2PfcMfhM/MqVURkLvBKQK/g3SgitwAPOOceLE5koxMR+TvgH4EmYDlwLvAo8JIihmUYhmGcABxzNibeJ03n3INBL11mtNGZyvLgEweGtY1PPbcf3vZqVkg5921rG9AH82hEuHhytW+y2Z1/UWNNvyab3RTrERSzXRHhlu0vkn3ZJRycP4dfbNvR94UDpD5ewSsmFFa7nnzyST772c/2TDYBrpm4sCRXOKHQzycSpXC/jrwz7/G+xhC/AGqAVvXKkN+XaH4DR/OPwFnAFufcpcBiYG9xQzIMwzBOBPpanrpfRH4J/Mo5t7U7U0QS5A+Pvh64H/jesEV4nKTTjkVVB4nvauvXvs22PYXjStpbCqtl6XRhv1+yrOdEGDLpOKfXV/DQL/9I5uBhZi2YQ6q9jIje2yXBu9PC8qOJkGMj1J41ve+R9uDzJfV+y1wqeF+lv7zaK6ePPNFHXaiVWx2Db99mRu3V1PtIQ47Y8O3tDNl3qknE1X7GSIqz6spY/tAjZLvSLFowh5wT3/PSezVTaiXWt9cypN3uIzzeMvk04pEsd+1e4zuHM4pwzcSFXDfpDDLZ/D3pvYJh+zZ1vt7fqPdtVqh9m3pPpj7OpEL1RZfa86r3bSZVPTqtj07R+zBjkeAjUlKqfj0h0Y6movomEQ0+ClHvKxQ3MOVBNqS8jkH3s++4G3WPnSo/rcZ3VtWT0XtBVVtlen+zytf9qfvNf6TKyE7kxpDZ7GDIOudu0hki8hbn3JeLFM9op9M51+l9kZR0zq0RkfnFDsowDMMoffqacF4B/C3wExGZCRwCyoAo+TM5/9M5t3w4AzxeFk5P0NwY77tgCL/bsZ9/W72dhdVVfP3UuSSjR3/g/eoZTTy8u4Xm6gnMrA6e/BnDx7tnTeGM2koSlLGwZnh8MSIiXD/1dF45cT4PH9zKgXQX9fFyzq+bTn28vGeyaRijiRPBpRb4h4C89494FGOHbSJSB9wF3CsiB4Ghl4YYhmEYRi/6mnDeAbzXOfdNEYkD44EO59yhYY9skCTig/vA9bm1O4j+zet49o9/5K97D/DSxqPPUoxHhKWTa0MPpTeGFxHh7PF1dKWO/4uF/tIQL+eVE+eHrrQZhjEyiMjXnHPvd849rPLOB94JXArMKlpwoxjn3Ku95E0icj9QC/y+iCEZhmEYJwh9TTi/B/xBRL4HfNE5t3PYIxoqUilkS14FnN2j5JR7CjLajgMFZ9Ou9sI+vM7OMk6qrubJ3/2OzOFDzJo7FeeEdKqwrzKrjieJxgpSxJjLqPxCOD4ZrZLm6Wv1qoSW5uojQ6SrcK0LkchqaasmElMxhMh6fceQpJW00HeEiapfxZztCt53qu8rbHIuviMw1NES6vgZLbvt6irsfQ2T0frThb7KKImlbisa0UdaFJ6Llp32luB2KqmuT1IbckSHPjZDy1+1jDaqykRC0lrGWZ3sDLwfLaONRYPlspGwY0vS/XmWhXx9L2Hoa6Mu+L60DFUff6KvzYbIU9Mh0tl29cxSPqmzvja4zkQkeFxGQ8Zx2MpihOB6huvLqhJf4bxCRF4L/AV4M/kjUDYD3wVuKGJcox4ROYP8dhgHPGxnaxuGYRgjwTEnnM6520Xkt8AngadE5Ieoo+mcc18Z5viKxpcWzeOx/QeZuWAG0ytL0xTmRGZLx0G+sO1xkpEYH2k6n/GJymKHNOTs6GznQ+ueJZXL8aX5i5hdacfxnQjkTYNKmquATwDfJ7/N443OuYeKGtEYQEQ+CbyOvHIJ4Lsi8nPn3GeLGJZhGIZxAtAffWAaOAIkydvP61fJEo9EuGjCOJtslii371tD7uQzaZ3ezB8OrCt2OMPCz3duZf/cebSdcSa3bd/a9wVGiSA4d/yv0Y5zbr1z7npgMvBvwBdE5BEReZeIlPT/lwbJG4GznHOfcs59ivyxKG8uckyGYRjGCcAxVzhF5ArgK8DdwBnOufYRiWooSEXIvJifLHbtV9JZ5UCbUi6vGSVPjccLstiMks7qdCQysA9msaSS3SYKUkft+BpRskTtTOtUu7l+SPBCPzQ6JauMqjJOy3S1jFbJa8OcZlW/9UceGBabdspNJAsqr7Tan6mlsykl+Uxp6Ww6WEabVi67IjC/vJ5nVz1JLptmfuNi4pGcT6aqXU61pLar1zmrYTJO3RP6jiuVu2xZNFi2G4sFu7yGndih+z2hrj25uppfrXoOiUY5bcoUop5MO66ktj65sOovFyIJDnOyjfRDKh0unVUxqP7V8u6wsZVWZfQz61Rjt0OltXQ2FSKj1W60MdHpwOLhMtoQCXRkBDxkx8LEcbA451qAW4FbRWQR8A7gWWBmUQMbvWwmb/rXrcFPAhuKFo1hGIZxwtDXHs6PA69zzq0aiWCGkzvXtfPRR49wekMb/3vBJBLRkf9A9uftrXzo6T3Mq0/w/YsnUhE7cQxont7Twbv+eoBxZVF+dPFExpf3NfSGl2vGz2d2WT3lkShzK8f1fcEY5OUTJzOlrJx0LkdTVYI3PLOMrlyWr500j9lVpSchNkYGEbkNuBrY45w72ctrAH4GNJOf2LzeOXcw4NorgK+Sdzr/tnPu5qGIyTn3PPAhEfnIUNRXSojI18l/B9YFrBKRe72fLwP+WszYDMMwjBODY854nHMXlcJkE+DDj7Th3vE3PNYV46EdbUWJ4RPP7Sf31qt4NlbJ77cUJ4Zi8W8rWjly1YVsnDqVn647XOxwEBFOrp5UspPNbk6uqWNxXQP/t20buxbM5dDZZ/KtLduLHZYxzLhBvPrB98gfmaX5CHCfc24ucJ/3sw8RiQL/DVwJLALe6K1MDhnOub7dq048ngKWAXcCHyN/dvYD5L9Q/l3xwjIMwzBOFIq7zDSM5NJROnY3AJBuL+OUhlaW/eJe0vsOMaNsDunOpE/Kp11hw6SFvvr7IUXUMsBYTji9Lsmf73yQ9P7DzFswnWw65pOShslrtTurcwXJqK4/VKqqZboSLJHV9ZML/g7CJ7FUUkTdb2ExuJxwZn2c5/7wBJm2Dk6+YBK5bNTfP0pGm+kKdgPOqv5JKSlvp5bahkhhtYxRy0I1XdlgCa6us6tX/2RCnGmTSkKppbNxCZZW6r7wH72i+zcw7H5xWm05dzy70pPXTuxpzyePDnn2WqYbJpGVkHsJk9pmekmTu/GNMyVbTysXHP1stKRZO9B2+qTOhXR7RsmDfY7Bhfq1W3GYEEKb8oS510bVs9bpoP4Zar2FG/IaVd3OPSQizb2yrwWWeunvk5/QfLhXmbOB9c65jQAi8lPvuueHK1YDnHPf706LSAKY5/241jmXDr7KMAzDMIaOkp1wbmrNks464t4nxu9eOIkHth9h5qlzaK5O9nH18PBfSybz4I5Wms+sY25tIYb7t7fxL0/tZW5tktsuaKQyfmyp7UPb2vmHxw4wqzbOD5eOpzox+qW5/7q4nosmtlMXr+eUccFGTCv2dfDux/ZQH4/x3XObGF82/OdrnihcOXkSU8rL6MrmOHtcbbHDGVE6c53cceAPtGaP8NLqS5mSmFLskIYV5yA38kcDT+o+Nss5t1NEJgaUmQq8qH7eBpwzEsEZICJLyX8ZsJn8dxzTROR6c/g1DMMwhpvRP1M5Ttqy8JftHT0/J6MRLp9ezcwiTTYB4hHhZU01vskmwL8uP0Dq9VexIl7FH7a29FnPx585TPr1L2dVeS2/3jg2pLkiwtKmytDJJsDnVh7k8EsvZOO0afx0w/4RjO7E4LS6Ws4eV4+EuQ+VKKva19KVKGPSpLN49MgTxQ5nRHDIcb+A8SLylHoN1dmWQQPvuKfGIjJdRN4tIv8lIv8jIjeJyGWedNc4mi8DL3fOXeKcuxi4HPjPIsdkGIZhnACU7Aqny+aYnqwi3Z4gm1KS1xBnSi3d1NLCXIhbp//iwrWxfkhS48qpNBLNcVpdOff9Ji+1nTt3Brl01Cdz1bJVEcfpdUl+95u/ktrfwrzZU8mk/CuBouScou43p8RTWr47UEfLqJJY6ji1PFj3c5hUGPWxMJuOcUZ9kmf+/DjZ9k4WnjGVXDZCVslcM6qetMrvUi61Wo6qnUFjkWBpajpE2pn2OdMW0tlefaXHh5bO6rSWWaZ6ueX2xOeTzqp+VJ/Rw8ZiWDqi7lnLi3O5YGmylrn6XFW1DFiCpbNR3xgNlo/65OaZYDmuRj9Lfa1+Bj5Jc06XKdRTG5nAodan6Og6yJz4dLqVutqzKy59y2g1sf7IaCN9z6XC7n2wDNKldp9zbskAr9ktIpO91c3JwJ6AMtuAaernJmDHQIMTkbnAzcB44EHgafLOqxPIH/3xJRG5zTn31YHWXeLEnXNru39wzr0gIiYjMQzDMIadkp1wzqtNMKM60XfBUcBXzpzMAztaaD69nnl1ZX2W/9LZE3jF9jaaqmpYWN93+bHC/zt5HOeOO0IlkzitwVxUjaGhKTmVv6m/lvbsEWYkZxQ7nFLlbuB68hPB64FfBZR5EpgrIjOB7cB1wJuOo61JwP9zzgUe6SEikeOst9RZJiLfAX7o/fxm8mZChmEYhjGslOyEMznAczKLSTwiXNZU61sZOhaxiHDZtNI731xEuGRKFenO4smejdJkYnwCxCcUO4wRYzAGU30hIj8hbxA0XkS2AZ8iP9G8XUTeCWwFXueVnUL++JOrnHMZEXkf8Afy+obbjscF3TkXepSHiFQBEefcjwZa7wnAjcB7gfeTlzc/BHyzqBEZhmEYJwQlO+H0ESJtS2s31BCJqZbX6oPoc0q+F1XyybRyrNUyxpjOV/LdWFw5gEa0HE9JLEPkivq+tHSRbLAUMZfpW1qs0dLZSFy75ipZqI4zq51sI4FlNLoeHZt2/c2qe9H56WywFFZLFBOxQsyZrJZeKidb9UzDXG19MtVe7elnr3/nk4Dq8uo5daj7ias+Kpd0YHlNLkQy6Rs3qkxaOfnq+9Ry5Li6F+3kq6W5vvsNGa96LIbJ02NqbGm0rDdMRtuftJbXVkRDHHRVu7GIzg+RDWtXZXW/8ZC0rkfLa2O6n720hDzn48Hhd9Edapxzbwz51UsDyu4ArlI/3wPcMxxxiciHgNcAKRF5zDnX2yX3hMVb9V3mnZv6lWLHYxiGYZxYlKxp0Oa2NOkiWDX25q9793PFY09x44q1dGbtiLjhYmP7Id615j7+ecODHEh39H2BAcDerg7e9fyjvG3lX9l0pHXE2j2QSnH98hVc++RTrGkd3nNZO3Nd/N+BX/M/e3/Ktq6dw9rW6EBw7vhfYwURubZX1pucc+c55y7h6HNCT2icczlghYhML3YshmEYxolHyU4423Lw8M4jxQ6D/9i4hcyVV/NsJM79e8x5dbj4we61dJ66hD1TpvC7vRuLHc6Y4Y5dm9nd3EzLaYv5zo5NI9buXdt3sHnKFFrPP59vbNsyrG2tbF9DW2U5dbPP489tTw5rW6OFQbrUjhVOFZFfi8hi7+d7ReSXIvIj4JliBjZKmQysEpH7ROTu7lexgzIMwzBKn5KV1Lpsjpm1MSSSw2UL0lktLQyTzmrJYZjUVqOlnr2ZX1nNkw8+QOrgAaZPnUoqHe+Xq2hOyWgjOeV4qiV7qoxey9XOsfp+cyFSW51OlHUV6tcSYmX76WtX94+SMfrK5EIkvmklbdUuwVktXQ6+Vss/nRMWVFSzauVTZFNdLJx+EhWJlM/JVktn/c6myvn2GG603fSWuPqeh8rXksawb3US0eAV70iIvNLnwMvRssze6PsPc7hdUF3FnS+sJhKPs2jcxB4prXZSjoXJaKMhUulc8JhG9e+86krSa9eR276dRTX1gXFmtetsyPsz45PUFpoqUzLaprIGHtq3jFT7ARbFp5AIkNjrLd/aPTiuyia0dFaC8/V4SEQLsuG4Suv8sD40+sY59xkRaQQ+Lfmzfj4B1JB3Yx3w3tATgH87notE5Argq+T33X7bOXdzr99fC3yG/J+9DPCBY+2zNQzDME48SnbCOa82TnPNyLnUPrx/LzdvXs/cyir+Y+4iktH8ROYzcxby8P69NDdPZ05V1YjF0x+W7W/h/63aypTyJDdMn8BNL2xjakWc/1kyjZrE2Boab508l4UVtVTFYiyqbih2OGOGl02YwoREGV25LOc11Pd9wRBx0YTx3JqI05LOsKRm0rC21Rgfz7hYDa2pIyyqmjmsbY0Wir+ZYMToAD4AzAX+B3gK+GIxAxptiEgZecOgOcBzwHecc8GbqI++Ngr8N3AZ+WNtnhSRu51zz6ti9wF3O+eciJwK3A4sGMp7MAzDMMY2JSupTUZH9tY+v3kDuSuvZkVE+Mv+vT358UiEpRMmjbrJJsDn1u3kyKUvZU11HZ94fgutl1/K8zUN/GrrgWKHNmBEhLPqJtlk8zg4rbaBs+snIDKycspTamu5YPw4IsPc7ooja+mqqmXCnEu4r/WpYW1rNODIrxQf72usICJfBH4D/A5Y4py7FlgO/FpE3lLM2EYZ3weWkJ9sXgl8eQDXng2sd85tdM6lgJ8Cvr2zzrk2Vzg8uJIT6vsOwzAMoz+MrWWsAeByQqYzv8KZ0m60SrqZCXGU1fla0qnp/cFsXmU1zzz0AKkD+5nWOItMNup3njyG7LabWEzLRPuWW4beS0i+z7U0E2NBeRVbHn6EdMthzqyv4ckHHiV9uIW5Z80kl42S1Q6dKgafCFDJHkXJA3UMuozP6Tfbt3RZ92EikepJ674qU/Uc6SqcS+qXZwbLaFPZYEltmNCxt9zVJxEOc5RV6bhyGc6p/XLxECdfTVw5x+q0jiGVCZbRhkk9tVxWy2jjykVW5+vn4ZekF/oxomSo2WzwBEaX19LfjO95qHGsxk06xJlWS2Vz6jNvY7yBtv1Pk+o4yEnxycQCnpOe85apcZz09Vs2MB0Jca+NqXoSseA+73adliH+jH6CfOK/3Dl3KoCI/Iz8yt2vReQe4O+LG9qoYpFz7hQA7xzOJwZw7VTgRfXzNuCc3oVE5NXAfwATgVccf6iGYRhGKVKyE86R5rNzTuLhA3uYNmU2syqH/ozMR/cc5iOrtjO7OsktS5qoiAUfCzIQPjp3Nhfu3cfUuVNYUFPFQ3sO0LRwIifXV/aUeXpfG/+0YgeTy+N859yp1CQG365hjCSzyqfytxOupDXbzrzyE8Ck04XvNy8xVojIj8krdVZ0Z7r8t3VfL1pUo4+eb4y8s1AHcm1Q4aO+z3DO3QncKSIXk9/P+bKjKhK5AbgBYPr0E+B9aBSd5mmNbNm2u9hhGIZBESec3t6Qp4DtzrmrRaQB+BnQDGwGXu+cO+iV/SjwTvJHGr7fOfeHogR9DOKRCEvHNw7bB72b1u7iyDWX8czDy7jnxUO8dua4QdcZFWHpxAkkvBWsSyeNI6lMgwA+vWYvhy67hAOrXuCXmw7wjvkTBt2uYYw0U5MTix2CMcQ4594qIqcA7c65DcWOZxRzmoi0eGkByr2fBXDOuZpjXLsNmKZ+bgJ2hBV2zj0kIrNFZLxzbl+v390K3AqwZMmSE2QR3igmW7btxn2p73LyweGPxTBOdIq5wvmPwGryroIAHwHuc87dLCIf8X7+sIgsAq4DTgKmAH8SkXkuTHPq4ZzQ1ZmXV4ZJTH3yPe3mGnJYfc71La+NhEgOfbrKEHmtln3GY37H15Oqy7n/vkdI7TvIrGlzyGRiPfe1s6OLcmJUxmJHxZ8NcXnV7qE63yeFBU6qKmP9g4+TOdzK3LOnk81EiXjld7anqE7Qs+qZ0dJl3W6ILFkTJqNNJgsT4KT3757OFFGJU5fIP7PWtsrAazVZn7w2WJLpe0QhYyCs/vxNqP5V2dEQuWxvx9ug9pLRDIfTnThgYqIwRrVjbTYT3L8xLcGNBrvaarlsQqeTKQ6lMqRzOaaUFeLxyc2VlDkSIvHVPaHfb12pwr1oGXAmF6Utk6Irl6VMygvlQyTX2i02orrB5zIcCX5P6mep3Wi1jDap+0q70apry5REuUxJZ5Mx5fQbOeafqyElv4dzxJobcURkDrDVOZdyzj0XUuZk59zKEQ5tVOKcG4ws5UlgrojMBLaT/3/xm3QB73ls8EyDzgASgJ0BZhiGYfRQlAmniDSR3+fxOeCfvexrgaVe+vvAA8CHvfyfOue6gE0isp68kcGjIxhyII8e2MOXt65jTkU1N80+mURk+OSmN58yjft3HWL6vPEsrCtMsG7ZsJNvrN9BAuH2c05lRmX5MWoZOJ88qYmLdx6isWocpzUU2v3sM9u5dfNhBLjz4iaWTKgMr2QIuePFvXxi5VYiwA/OWsSp9UMvXx5NPN2yi89ufgwHfHLWEs6pmzwi7T61v5XrH1uHA758xlSumT4yZkzrjxzkX174C1ng76Ys5rKG2SPSrjGmmAL8QESWAfcDLwCd5PcPLgGuJj85ekfRIiwRPAnu+4A/kN/Kf5tzbpWI3Oj9/hbgb4C3iUiavGvwG5SJkGEYhmEUzaX2v4D/h39RaZJzbieA92+3Bi7ItGBqUKUicoOIPCUiT+3v6pfr+6D44tYXyF52JcslyyMH9gxrW/FIhJdPafBNNgHu2nWIure9ldjsWTyx/9CQtxsV4aVT6n2TTYAfbW+j5uqXEZ/dzDdX7Q25euj51Z4WKt9wDfHzl/DAnrHnpjtQ/tqyg+pzl1J74ct44PCuEWv3z7sOkbj0HCrfcCV37mobsXafPLyLigWnM+Flf8ODrdtHrN1So5Rdap1zDwEXAr8nv1fwm8BPyH9BmQD+1jlnk80hwjl3j3NunnNutnPuc17eLd5kE+fc551zJznnTnfOnWdncBqGYRi9GfEVThG5GtjjnFsmIkv7c0lAXuC3p3qPyCl1VS6Tzt+elsumlXxP54dJT7V0sff+zDnl1Tz7yF9IHdjP1EnzyOYiuDDJpZraO+Xc6ZMHKkmjzs+oODNaHpyN8uZJjfz7d75LXVkZ556xmFQ67nN81cSU3M/vMBrs0Osrk/V/N7G4qpxHH3ycXEsrL1s0jfa2ylDX0lxOS3mV862SIiaSBQfaaNnRLp6Q74d3Nldz40/uIhmLcvlZJ/vq7o2WzuZCnmlGO7yqfvNJo1WdHb3k0NGQcpqEltqqR6OdSXV72gH18oZpPPjYAzjnuGLOBT3jNBMimy5XYyjmc1JV7qnagTbEjfbVM2r48YOPcyST4+3nNRdi1hJW5cKqx4fPgVb1V2dXsifdlSmM6Y50QYp9Vs0MfrH2flqeX8brpl5Ih/ce9jnuqhjCnIH9qDGinZd1n4e40WoZrXYY9ktng2W0WsYctre727XZBf6ZO3769jwe2zjncsBvvZdhGIZhGKOYYkhqLwCuEZGrgDKgRkR+BOwWkcnOuZ0iMhnoXjIckGnBSHLT7FN55MBumibNYVblsXwXho+ZFeVMrq5mRnk5NfF43xcMEd8+ay5/3LGfxvJGFjeMnKz1ZVPqWHVtDZ1HqohFRv9qzGBZVDWen596NQBxNVvdmzrCf2x9kqxzfKjpbCYnh/YZLKqvYMXVJ5Nzrl9n2u7vSvP3yzfRms7y5ZNmMa/m+CTWTWU1/OCkV5J1jlSmIA8/ku3im7v+Qku2g3dMOJ8ZZeOPq35NZy7F9/c+wIFMG28bfx4zyycNuk7DMAzDMAzDz4hLap1zH3XONTnnmskbEPzZOfcW4G7geq/Y9cCvvPTdwHUikvSMC+YysHPEho1kJMql46cwu0iTTYCbN22h49KX8lxZGX/cPXL231ERrpw6fkQnm90ko5ETYrLZTTwS9U02Ae7cs44DU5tpmTmfn+9dO0ztSr8mmwA/2bybdVOb2H32Er6ycXDfB0UlctR+6IcPrWNPbRUsPItfHlg+qPq7Wda6jt2VcZInnccvDy4bkjpHBzLIl2EYhmEYxtAxms7hvBm4XUTeCWwFXgfgGRTcDjwPZID39uVQm79OSKXyUj3tjtmdB3lHzG58kssQeW1/9jfpwLRMMszJVnyutoV2U1pG21F4TDq2iDjmlFWx87FHSR88yPRFp5HJxHwxx2LBXSUhsZHVUtjgftDX6nh87rghrqJRJcOsrmrtScfVcSzJ6vaedFdrRaHOzr5j0xLOMMfgbEhaizO79L0c47mnVDmf3FSV0dJNXW+ZklxqGWdUtINwoR4tBZ6WrKNjyxokEmVO/XQSnjxZu6Fqd1yd1lJbLa/1SWTDxqt23FX3Mq+6nOyaDUS2bmd+XU2P9FvLaNtThXSHGt9dWSW7Vc9P3+/Uslrad68i3bKfRYlJPX0dDYlHEw2R0U4vr6F919NkO1s5I1lPhdcXEiK1rVByWS1d9rnRqr7Vkukwd+NuybiECrIHjqP0JbWGYRiGYYwdijrhdM49QN6NFufcfuClIeU+R97Rdkh4aN8e/vPFjSysrOGTs04iFhnxhd4h419nLeCh/XuYOnUGC6prix1OKGtaW/jkhrWMi8f40sJFgfLf5w908t6ndjO+HL53aQMN5cPn+jvWOb26kfr9L5DN5jirurHY4fDyKeOoT8RpzWQ5r27opamnVDXxL5GltGQ7OLlyaA6Nn1cxmX+a/DIOZo5wVk3TkNQ5Whiu84ANwzAMwzAGytidaQ2Cmze/QObyq3k83cmTh/b1fcEoJhaJ8JIJjaN6sgnwtRc30XLmOayrH89dO4LdRz+7aj+tLzudtZMn88PnWwPLGHnu2vcCR+bNo+uUU/jZvnXFDgeAs8bX8JLGeiIyPJOdORUTOaN6BhEZuj9bzeUTWFzdTHQI6yw6Lr8yfrwvwzAMwzCMoWQ0SWqHFOekR17Z2412ZkUVax95iHTLYSZPryTnxCcNDZOJ5vqxvymipHFa1qYldbEQ98qulJLRKrmvjidMJhlXjq8+uazPxbVQp3aI9d9jgTC3W1957aabC5bRZnMRmhM1rF6xjOyRNmbOnU8uJ4hyrE2Ud3FKQ4TlD6wi3dLB3HMayHbFyWqH4VSwq7B2PE2rGDpUfpd2TtWuvDrtu5eIKq8luIQSU3OWuLo33V5MPzOV1uNGO5amQ6TD8yvKeWDDGkQizGtoosyTePqks0peqx1oo1r2GSI77g8+KbN6TinlOtup0l1KLqufjU4PFN80Ufetytb3qPs86pNAB7sEazdanR8mo42ESGfDZPQ9aVuQNAzDMAyjRCnZCeex+MLc0/jL/t00T5zJ9IqqYodzQnBj0wJOO7iLydMdp9XWB5b5xJn1nL3lCLXxSs5trAgsU0rsTbXx1Z2PgYMPTzuLCYn+O7teMaGZSckKss4xu7KS9619lIxz/NusU5hSdnwOsSNJa6aLr2x/jNZsihsmnsu0soZih1RS2EKlYRiGYRijhRLSkfWfsmiUyyZOYVblyDusnqhERbiwYXLoZBMgIsKVzVUnxGQT4M79qznQPJODzc38YoBOsyLCGbWTOKuukdt3b2R78wz2zJ/P93ZuGKZoh5Y/HVjPjro6uuaezk/3P1vscEoKR155cLwvwzAMwzCMoaRkVzgdEigJ1XJFLcvUTrbaHdPvWNt3u3r7mpbRxSRYRqsPvfe5vKoYtGup/kCo5ZO6zkhIvqgYdFsZJXXsLYUNisfXVyFlNOXxVCHmaLBrbuuB4D2o6a5C/2iH4faOwhmNKRV/SsXWrqSanb7nq6SzKt2ZDXamTSlJbe8P5D4JZYg1aDyqx0EhPSNRzWMbVwMwrW52T5vaoVjLL8uVjFNLQGeVVfH79WuRaJS5DY3EvT7WUk89ViKRYHmnHjdh0lCnx01a9bt2ndXScO0QrdxoG+N1dOx6ivShvZyRCDYZCnNa9Y2yEEfZaIiMNhKy9pfwOQYXZLRJLamNBcvWfXLZEGfaSEj/97zfhnhJ0lY4DcMwDMMYLZTshHOkeaZlN9/cuYpZ5TV8aNrio85NHCzPte7jK9tWMb2sgn9tPp1k1BxcxzpXNMyjMVGFIJxRc/zOrq+YOJ1JyXKyLsdF48cNYYTDx5k10/mXaIK2bIrTq2YUO5ySw8x/DMMwDMMYLZyQktrh4Os7niN34ctYQYrHD+0c8vq/uv15Uudfwsp4hAcPBLu8GmMLEWFx9VROr56CDMLZVUQ4q24i59Y3DqqekWZhZSNn1UwfNlfbE5XucziP92UYhmEYhjGUlO4KpyvIRsMcaMMlo8H52ZCD5UUc05I1rF3+KF2H9jNp3CKyOSESDZDO0ctVVbWl5b7azRQiNJdV8/TyJ0gd3EfT3PyKkJZ36rT4ZLSFGLR0Nq3kjVqSqglzoA3rH42WIvqlv4XybW0Fc5tYiNRWO55qqWZ7KllIqzLa8TQV5rIb4kabUdLZrpD83h/Io2qulBP9/JScVbsG+yS8qt/V7WsJdXm80I8uxMo0zJk2TL6snYsjkb4ne77+0jJaJXHWLrUZnyNwIa1dnrXMVXdq2HjyOc2qtO4rXWcs2h8ZbaFvdXntQBvXjr79EKqGuVH7ZcxaXpvrrtwwDMMwDKMkKd0J5wjzoWln8dihbcyYPZ9ZFeHGOMfLv0w7nUcO7mDahNksqBr6+g1Ds68zzXuWbaM9k+Ori5uYVV1W7JCMAWCKWsMwDMMwRgs24RwikpEYlzQ0+1ZchpJEJMrScdN8520axnDx3fV7WT11GpGGOr64djXfWjK92CEZAyBsNdwwDMMwDGOkOSEmnGGy2Fw/5LXpECmplgdqh0s94cwq6WLKxVV+sGxQyyE1YZNYF3KEgU9Gq2SlnUp6Gu7KG1yndhjNhrSr+0HLGNOqrbRyl42GuKL6JJzqWi1F7khrB9qYSge70aZzwQ60GbUUpB1rsyHms+lejyKtxkF1XMs4C2R9Mlrl2qpiKlN9F1MX6+cRD9lxrZ+3Tmv5cs4pB9eQa/V4mlNZQXbtRlxZkjkNlT1uwT7pbIhbsX7ekRD5azZk36aOR8fpd53NqnShzkQ/vozRYzSuZN9aOhsZ4JdGehz7nWl1OswxOJ/uj1y3v+SPRRmy6gzDMAzDMAbFCTHhHEqea93Jd/c+S3OyjhunnE1MRo/v0nMtB/mPzS8wvbycz85dQJk52Q4LhzOH+V3L/USJcGXtS6mKVvZ90RjjmunjaEjGaM/mWDp+fLHDGfN0ZbN8bO06NnV08Kk5Mzijvm5Y2xtOl1oRmQ/8TGXNAj7pnPsvVWYp8Ctgk5d1h3Pu08MXlWEYhmEYo5XRM1saI/zP7qeRMy/lWXeEZ1q2FTscH1/cup5DF17E04k4f9yzq9jhlCyPHllGtGYqmcp6lh1ZXuxwho0LJ9Xy8in15iI7BPxpz16ejEY5eOklfGb9lmKHMyicc2udc6c7504HzgTagTsDiv6lu5xNNg3DMAzjxKVkVzgd0uOWGSb901JSvSKgJZ3OJ7mMMDlRw5Y1T9DVeoDGcVVEcD75qK4zlQuWqmqZoU+OqyR40UjfMlp/nEJzsoJdzywjtX8/E+cspiOVpEtJIH3y4BA5cTpE7qvvS99vWJxhbrFhcxfdz2ESaB2bltHq+tO9+qQnrdrSEtewb1x8kthe19ZH6tl6eC05l+G0yjN6pK7Sq1whVtV3qlBlbGD7cbXsMhIiR9b9FY0Gl9FoWbkL6btQGW1Wj6dCWo+JXC44hohPBlyIJ+K0dFbLZbXkuJBOxIJl6LqMlsvqPdBhUtiwfgiTLoe5AetnNLMqSWr9BhJPPsXsqiRxz324W2orIc/neBnBPZwvBTY458b2LNowDMMwjGGjZCecw8UHpl7AE4e3MGPGQmaUjy632H+dtYj79+1kYsMMTq5pKEoMm9oP8V/bn6EhXsaHpi2hIhrv+6IxxpmVi6mP1RIlyvwKM9MZLEeyKb6+41Fas13cOOksmsqK+77qyGb4xLo1bO/q4OPN8zippm7QdZ5aV8Ntpy9ia3sHV00d5vemG15JbS+uA34S8rvzRGQFsAP4oHNu1YhFZRiGYRjGqMEktQMkGYlxUf1sZlWMK3YoRxGPRHj5xKlFm2wCfHvXKo4sOoP1VeXcu29j0eIYTkSE2WWzaS5rRkxuOmjuP/ACL9ZV0T7/FH6079lih8Mfdu/kmfIkB847ny9u3TBk9Z5aV8PVUyaRiA7vn11H3ujqeF/AeBF5Sr1uCGpHRBLANcDPA379NDDDOXca8HXgrqG5O8MwDMMwxholu8LpKMgLcz73Ue3cGSxPDaNMSSC1rFQ71mazwZJd7azplwcq99AQeWoYWj7Zmim4v2ZCYsiGOLVqqa2W/uprw+LU7qEd6TiT4zVsWv8smSMtjJ90Fh3puF9i2Y9n4ZM3q77V0lndU2ESSJ/bre9+dQxhbqmFdFQ965q4f+nILxMt5Gtpr461LKQvwsafz+U1EixJDSOXC65f+iGj1Wk9bjKZwp8MLcfVEtYwGap2qY0qs62pZVV07l5HpuUAM5L1Pe8z/T5JqvdeMpYObCsW1e+xQvlYTDvTFq7tLUnvZkZVkvTqTZDqYlZFkng8fdS9aCREKqyltjrtk0N3y56H+HuLQa5w7nPOLelHuSuBp51zu49u37Wo9D0i8k0RGe+c2zeoyAzDMAzDGHOU7ITTKA5vn3Q6Cw5vpa6mnKpogg9vvpf6WBn/NPXskpTXGoPn7NppfCgSpzXbxXl1TcUOh7MaGvjvkxaxq6OTl02aWOxwRjNvJEROKyKNwG7nnBORs8mrafaPZHCGYRiGYYwObMJpDClRiXBBXTMAn37xQdoXnM7BXVu4b/8GXjlxQXGDM0Ytp1Q3Av1buR0JFtfVQV2xozh+hrsXRaQCuAx4t8q7EcA5dwvwWuA9IpIBOoDrnBvBnaWGYRiGYYwaSnbC6Zz0HECfyYW41CrJoXZ11B96kyGHyadyha7TElbtpKoluGEyWi21DZPsabmfvpdOJW/UrrNh8kxfPS5YXqvTcSWBDJN2ardY3Z8Ak2PVbNnwHOn2VuomnE1rOuHr/zBZbFi+JsxVNBsio82EtBsmYtZS4WREu5P6y/ldjINjTWgpbEh7YQ6u0ZAxkQ1xEw6VxWa1FDanygTXI9KfMiq2MBmtT4pdyI+5YNfZuHLW1dLZWIgTrM+xNl54v2npbKiLbD9k9Jr+jMWYlt0HSWcJfp8PrUut+KTow4Fzrh0Y1yvvFpX+BvCNYQ3CMAzDMIwxQclOOEuZ1W37+dr2lUyOV/OBpjNJRKJ9X1QE3jrxDOYd3kJVdQULKycXOxzDOHGwtUTDMAzDMEYJ5lI7Bvna9pW0n3oeK2NZ/npwa7HDCSUqEc6vm2mTTcMYQYbApdYwDMMwDGPIKOkVzm4XTZ/LZoj0VMsYteRV5/skrEpGq+Wp/ZHRJvpx+LymK1Mw22lLJWiMV7Nq3Qo6D+6lbupCjqQTPmln2EkdYe6vWoYalXD5aDcdaSWjDZHmamlxOsQdV3+4DXOL1fHEdGwhMtqwtO7brM+ltdBWPBIsZY151XTmUjgnJCKF59EZIjdNDlBG63M6DXNA1pJltaidDXGLzShXX50Owz8WI3Rms2Sco65MyWJ1u6pO5xtD6smGNBuLhPRbPE1XNkva5ahIBLvs6jgTiVRPOq7ee2EyWi0PFvE7x3Zlc3Rmc9SVKQl+Jhpybc53bU86qtOqfvV8fe7EXh+GyXUNwzAMwzDGOiU94RwMK1r2cMuu55lfUcs/Np3qO8ah2PzDlLN4+NAWGqYuYH7lieWiuTt1iB/te4SKSJK3T7iQimhyRNpd076V7+25F0F416SraC5rHJF2i8XzLYd593PLyDrHFxYtYunECSPS7sYjrdy46jFS2SyfmLuIKydNGZF2txzp5DUPr6Y9neFTp0znjc1j+31l9jyGYRiGYYwWRs8sapTxle3L6bhwKY+kW3nq8FHHzBWVRCTKpQ2zWFA5qdihjDh3HXyazunz2FlTwUOHnx+xdp88soGpJ1/BhHkXsqJ944i1Wyzu27ebxHnnUXPNNdyxb++ItfvXA7uIn3Ya9a99HXfu3zNi7T64+yBy0lzqbnwTP95xaMTaHS7cIF6GYRiGYRhDScmucDonPXLPjE/eGazxi4hfgjc5WcXm554m1XqY+olVpLJRUkpCqKWOWkarXWf7I6NNqGtFfdzrSCdUuiDh1PGHyVD1p8b+OGuWKelfzudyWijfmQuWT2pnVt0/6RC31LSShaZD2opq5ah2o404xkdr2LJjLemuIzTULiHtpJdMWl2rZK06Bv0ti0SOls4GsaRiFt9f+XsE4eqJV/XEruOLq+slxNU3rl1bfTLaAmEOo2FHhmRDZOI+59+QceCTkqtx8NIJE7n90UfpcI5XLTwp1Km2Gz2mffkEvwd8btGq7gsbJvGjFY9z5Jmned/chT39mIwXXGe1dDau8qMxNY6V5D2nxm6YFHbp5Fr+6y9rOLR8Ne8/ZVpPu7pOCHaU1tJZnyxZPXdfPNkAye4QzvQc/veBYRiGYRhGMSnZCedg+eSMs/jrgW00zZxJc3ndsLb1XMtBvrT1BWaVV/DxmYuIh+xvM+Dq+iU0tW2koiLOwsrpI9buwsoZfKrsLaRdlKTaw1mqnFRTy73nXUTGOWoTw/tnoi2T5tMbV3IoneZjMxdy95mXksrlmFA+cvsam6vKePzlp9KRzVE3iPvtzOb44DM72HAkxX+cMpHF4yuHMMr+Y/NNwzAMwzBGCzazCaEiGuflE2Yyr3Jc34UHyX9sWcOBCy/kUbI8sG/XsLc3lolIhDOq54zoZLOb8mjyhJhsdlMejVEdG/77/fWurayqqmb3glP42ovrKIvGqIkn+r5wiElGI4OabAL8dutBHiDB9kvP58PPji4pvmEYhmEYRjEo2RVOR0HimfbJQYPLawdaLfHTUkQtly3XB8srmZ5fPhkswYv6XHBzTEuWs+LZ5aQOHGDCuJmksjE6lYxWS1W1U2l/nC210NEn/9QxqPWQtAt29NXSWZ3fFeJAmw2RzmpJbdgqTNonr9X1BF8R5Cjbu60wJ94wGa2WJOo+jPeStYYdI6Hr1ddEQ5xpw+rRz0a7Busx6rLa+Vc9p2zw90lRnxtvoWUtqdWutlGnXWq1JFXVEy28HzS6Tu0cq8duU3k56a3rcG2tzEiW98SkHWiTiUL9sXhwWy7kvRGJBDvW+q4NcTSO+CSywe7VuvyMmjjpVXuIP/kcMysTPTFpR99cyHMZMpyZBhmGYRiGMXoo2QnnWOIzc07i3r07mDxnKouqG4odjjFCHEof4ba9jwLw7knnUhevKHJEw0trOsOHV2/kUDrD5+bNpLkqf79LxzdSFolyKJ3iZRPG9pmtZ0+s4kfnTWVzaxdXN00tWhw23zQMwzAMY7RgktpRQFk0yisbp3FKzfDLd43Rw28OPsfeSY3snTiJuw+sLHY4w87Ptu5k2bh6Np55MjdvfNH3u3MbJnDFpKmh53OOJc6ZWMUbZo+jPFace3HkV8yP92UYGhG5QkTWish6EflIwO/fLCLPeq9HROS0YsRpGIZhjF5KeIVTAp05cyGuqkcyBQmr/tBVo+R72o02TEYbRpjD6JGusp50l5L1prIhEt8QB1Mds/6YG1dxahmtrqdTtZXWjr79cKDNhshWs+p2w8qHxd8fZ91ERMseC2W6fA69hXR0gNJZFyLBjfSqx+d463OsDZbR6vSEWBWduzYB0Fg5s6cuLaPV8mt9P1oum/P1u5LahvSjlsiWxwppLaPtD355rRpn6n2SSBZksXMaILdmO9GDh5hVEeuRzNbXtvSU0S6ysXihnpiq03e/meA/YRIiee0P2nU2TDqr8bkwpwvx5EJktP2Rwg8Wk9QaQ4GIRIH/Bi4DtgFPisjdzjl9JtUm4BLn3EERuRK4FThn5KM1DMMwRislPOEcOGvbd/Lz/c/QnGzguglnE5H8h8TVR3bxw33PMr+ihvdMOY2ojP1VGKP4XFZ/EuNilQhwXu3ImyABHE53cfPWFXS5LB+dcRKTy4bPVfXq6fVUxiIcTGW4fGKwdLYtk+HDKzezL53hCydPZXZN+bDF05HJ8pGVW9nWmebfT5rCgtriOMoOBzbfNIaIs4H1zrmNACLyU+BaoGfC6Zx7RJV/DGga0QgNwzCMUY/NnBTf2/Mo0XnnsTy9l1VHtvfk37L7STrPuYBH0odZdnhnESM0SomICEtqZnJmzUxEhn/VK4hf7N7Augnj2DZzFv+7Y92wt3fplFpe0zyOWO+lYo/bt+zm8dpaNi45hU+vHd732q+27eOv8XK2nLOETzy/Y1jbMowxylRA69+3eXlhvBP43bBGZBiGYYw5SnaF07mCvFDLOMNktEcyMepj1RzY9hxdnS00jMuvdiSjGSYlKtm29llSbYeZMLEcwRE2PwiT3emD7ttThSMftIOudn/N+Zxdg+V4YTLahHYGFS3JDK5Tp1Mq3an6qitUOqviUbeeDZXFFtIupHxKmYpqd9yKmC5fSHeGbDzTMlrRaz46/uBLjymjDSunJcthbrSamE9qq9xQVb4eH/ohh8loNbp/Yyo2PT4aExWkd2wikkgypay+Z5z6nFp1WlWq3V/DXGS106x+DyTLunrS0diRnvTciVlyy3cQbWtjTi1U1LTl7zdd6IdUKvjIFFEya+nHGl9TZYLMhheJZbM0V8aIerLdqM+ZNrgeLZHNaIfrjHbE7vtLhB4p8hB/35CzJU5jaAgamYGjS0QuJT/hvDDk9zcANwBMn14cRYdhGIZRHEp2wnk8vHPiS3mmdT3NUxYwtazgFvvhpvN4+NAWZjdPY1ZFfREjPDabO/bx/X1PMzlRybsbzyIeGdievNHEwcwh/tT6EGVSxkuql5KMJIsd0qjiULqTr25/mozL8Q9TljAucbTD7eFMJ9/Y8RQpl+MDUxczIXG0ZPSKCc3UxpJ05jK8ZHxxXFXb0lk+sGwXe7uyfO70ehaUw4HDh7j+tOF1rL1oUh3fOk3Y2d7Fq2aOP+56urI5Pvbci2xsT/Fv85o4qa5qCKMcOA7bw2kMGduAaernJuAoOYCInAp8G7jSObc/qCLn3K3k93eyZMkSG6GGYRgnECapVZRFEpxXu4hZ5RN9+RXROJeNm8PCquP/UDoS3LZ3GW0nL+a5aIpHDm8udjiD4i9tjxGtnsrhuGNlR+k7uA6UX+5Zw6aJ49k2dSo/3ft8YJlf7V3DxgkN7Jg+jR/vWR1YRkQ4v2EKLxk/3Xfm6Ujyo3UHeLS6jo1nncx7H9/LhkkTab30DP7t2QPD3vYFE2t5bfNEktHj/1P42237+XM2zpazz+Pja7YNYXTHjxvEyzAUTwJzRWSmiCSA64C7dQERmQ7cAbzVOfdCEWI0DMMwRjknxAqnlhx2+txfCx+wq7QbbTRYaOmUusjpJQSfTFTJVlW7fgda5V4Z5job4pLq1EfCnKo/FskxKVHJmk2r6Tq8j4lT5xCVnC+eLiXP1DFoGe0RFaeW0aZDJLW6G7SyVctZo2HunqpMNlv4IeOE6kgNm9u2kMocoa5ylk9+mo9Z908hPx7R/aNiC5E3arlsPCROXU+8l8RSy2jDpmt6KhPmWBuG7l8tr50QryK1azMSiTK5rNHnbNvN1GQ56d2biUTjTC0f75PSBrclgWndd1pqG1P1aRltVDnKahltLKbKK/fameMdmWV7iLa2M6Madm3dA20dzKyO9NSVTRXk7xrdlsuFSYuD449qN9posKRZu+bmVP/rPmlMVJDet5HYyudpSiZ7JLaRSHA92tFXtzuUmKTWGAqccxkReR/wByAK3OacWyUiN3q/vwX4JDAO+Ka3Fz3jnFtSrJgNwzCM0ccJMeE8Ubix8WweObSJyVNmcnJVY7HDGRTnV53L+M4XqCxLMjPZXOxwRh1XjptLXayMnMtxUX3wfqgrx8+iNpYk7bIsHTd6jSOvmVVNWTTC/s4sV85u4M33HmBfawtvP3VCsUPrF2ePr+Vbp8xkW3sXV06eVexwDGNIcc7dA9zTK+8Wlf474O9GOi7DMAxj7GATzhIiGYlxacNc4pFjr2SNBaISZWH5wqNWNo08IsL5dfmJZtgZryLCBfX5iWZ/VlOLyctn5PeX3rJyP2saJhFtnsxNy1fys5dMKnJk/eO8CXUAZAd4lqlhGIZhGEapU7ITTkfBnTYV8iGwUsnxtItnzOcY2ndbfhfcQpdq+W6Y02x/DpPvD2HSS+18m/XJfQtpLaPtzGoJrnLizGnJa3AMWm6qJzgxn0QxOGaUtLBWguW7YeJDLaTUvaarT4dIcPVkzScJ1umQ++rdXiTEmdYn41TjLOsbB1q+XGg9EuJkG/e5zhbGcYVyhc2FSGT7M7b0tTqGuJLO6rTvWvV+i6n3WESltRRWM7msjOzqvKR2WlmCdFfiqJh1H/ruJUQy7HPc9Uln1ROPBD8vp9xonRpD+h599Q9QRhvxYg77O3A8OMLfK4ZhGIZhGCNNyU44jWBe7DzAD/Y+xbhYFW+ZcC7xSHGHwL70IX516GHKI+VcU3cRyUjwkRdGOIfSnfzn9hVkyPGxGYuYkCwfdJ2tmRSf3/IcHbks/zprPpPLjnbBbU1n+MiqLbRksnz+lKlMqywbdLuvnFlNMiLs68xy7bSxIakddThzqTUMwzAMY/RgLrUnGN/f+xTts09mTayLJ1o2FDsc7jn8OKkpM9lVDsvagt1WjWPz8z3rWDtlHBtnTuW2neuGpM5f7trIynG1bJo1k/9+cX1gmR9u2snjdQ2sWTCff1+7c0jaBXj5jCreNL+WxCCcY090zKXWMAzDMIzRQsmucDqkR5qopYiJeEF2p/MjIe6hYVI3LTnsyMQD0+kQ10zfnrsByhu1VE5LMrMSCSyjJYdZJ9RHq9i/Yx1dLfsoaziJtnScdiUb7MgoZ11VkZakapLR4P7R7q8xJTP0SyMd42NV7N63iXRnCxOqp1MezZES7SpcqEfLXBOqTt2WjlOv8ui+0o6uWVUmzO02eQwnWi3vDHOj1c+jU0kxdV8kVFrLZfUYjaoOiKl9us0VMbLbX0RiMZoqaknG0t69KTl1LjgdJu+ekiwju2sr7uABppWXE/dku9qZdnKynNy2nUQOHaapLEHOG0exuJLRhri/ZpWMO92lxm4m+E+SCxl/PumyHhN6H3OIXFYiwWM3rK0wKbLf+bZw7/549HMsxGZ7Pg3DMAzDKHVKdsJZTNa37+X/9i+nKV7HmyacRVRGz0rNWyecx+Mt66lsWMj8iml9XzAI9qYP8ptDj1ATLedvGi4gETn6aIur689hSusLVNSUs7BixrDGU6q8ZvIM6uIJMi7H5RMnD0mdV0ycRkUsRkc2wzVTgqWtr5w6kfJYlLZ0hlfNaBiSdo2hwJEzTa1hGIZhGKMEm3AOA/+753GiC8/n6fVPsqh1K2fWNBc7pB4SkRgX1S3wGQUNF787/BhdTbPYeuBFnmxdzQW1px5VJiZRzq5ZaCYng0BEeOmEKYB/RXSwdV4yLl9nLNIVWuayxvFemWATIGPkMWmsYRiGYRijiZKdcEZwJD15W9iREBLiANofwmS0ndko9dEqdm5fQ1f7YaprK0k78ckt+9NSbylsN+kQ19kOda2WZGZUmc5ssOusdqZtz+r8Qp0+iaxKZ9XNZEXLVnPURSrZu3cjqfZD1NbO8mILljfmtAOoelxh0tlI6Edq7bIbLIFMqzIxVU88RFbtc9yN+J+eT0arHWhDnlMmRGatXYNRZURJlvVz1W60Ycei+MZ0yCJ7mLNutywX/E6zGe1Aq+rxy0oLA0dLW7Xja6arcHUqlVDlg98dEgmWv/scYlWcYdLZ/rjB5pyWHIfI4vU9hrjjhsWZUV/2dL/PB+pK3Re2wGkYhmEYxmihZCecxeTdky7i8Zb1TJo0m5nlE4sdzoDZn97PA20PUylVXFh1MTE59jA5kDnI/a0PUx2t4PLai4h70tmr68/nmba11NaWs6iyuR/tHubOg49SJmW8su7CIXesbcm08sfWv5CQGFfUXkJZJDmk9RuGJp1zfPzZHWw4kuLTJ03k5PqjnX6HCzfMa5wishloBbJAxjm3pNfvBfgqcBXQDrzdOff0sAZlGIZhGMaoZPRsLiwhyqMJltYvYn7llGKHclw80PpXEtVT2SstvNC5pu/ybY8g46axK9rBs+2re/JjEuWs6kX9mmwC/ObQE3RMnMbOshxPta063vBDeajtcVK14zmQjPJU24ohr98wNPe8eJDfdUVYd95ZfHDFrmKHMxxc6pw7vfdk0+NKYK73ugH41ohGZhiGYRjGqKFkVzgj4qjyZIdhh9j3Jz+rZJlhMtquEJmkRgsFIyHyOZ8DraozHSLJzIUsYnTlCrLHbKg0t5BO5fwy2vJIFfuPbKM9dYjKyirgaHfWQsxCVaSSHS1b6ew6SG31QiK9Yk4QLC3UfZt1Qk2kkp0HtpDqaqW2cgYRCZdD+++LwLS/PFRFqtjRsp1stpPa8kai4ohpKWgkWF6bUHLOeC/Jp74fLYtMqzI6X0ucdb5vLCqJZlJJUsvjhVqjPufc4P71OQIreW0spuSgWpKqZauRYAmoltf6yis32qiqX0tS02ntTBu8ep1JF95X2vE1oiyEtXQ2rN1oPFhe6+tzLfFNKUOrECm2r/7AEn4mV0XJ7D9E7Pn1TC6L+97TvWPrj9R3IIyCPdHXAj9wzjngMRGpE5HJzrmhOz/HMAxjlJGM5f0N+mJG0yQ2v1iSX0QaRiAlO+E0jp+Lqy5hbedqqiqrmJFs7rP80uqLWN2xhvrqcuaVzzrudq+qP5fJbWspi5ezsGLmcdcTxvlVZ1HfsZbKsjgLymcPef3G8XEkk+Xjq7ZyIJ3h0/NnML2yvNghDQnnTarif89qZFNrF6+a1zRi7Y6QaZAD/ij5mfL/OOdu7fX7qcCL6udtXp5NOA3DKFm6MuC+1Hc5+eDu4Q/GMEYRIz7hFJFpwA+ARvJfxN/qnPuqiDQAPwOagc3A651zB71rPgq8k/x+ofc75/4w0nGfSMQkxknlpwyo/CkVJ1MRciZn/+vJS3CzQ2yg0k1UopxcsYiyQcZpDC23b9nNX8qqkFlT+OwLa7h18bxihzRkXDK5hksmQzo9sudtusG5Bo0XkafUz7cGTCgvcM7tEJGJwL0issY595D6fdCb2N54hmEYhnECUowVzgzwL865p0WkGlgmIvcCbwfuc87dLCIfAT4CfFhEFgHXAScBU4A/icg851ywNaSHiOs5rL4/aKldKlvoli6VTmWDpapaKKeD6s8G2f7IaLM+6SVH5WdclqyLEgmQcejy+tOelp7qMmUhn4sjPtkmPe3GRIh454yKT4Ya7C7bW0YbRNQngSzkZ0L6RDvupnX/hOgKtZw4qt4Bul0tnS3zxlHG5UhEs75zVTvV+BiojFHHodWWNVG/jDbj8jXHlLzWJ6nVslgtzQxxSY0raa7fKVjLX+NkcjkcEFcPMMyNVkttw2SrOfX+0fWMj5eT27GTSFeKyYkE6Sxkc46KkHZ9MtpEWuUH/0noTzzkhGzOkXGOpJLv6vr75XCr6s8qebDvufgcbnP9rnsgDFJSuy9kX2YPzrkd3r97RORO4GxATzi3Afqg3yZgx+DCMgzDMAxjLDLiE05vD89OL90qIqvJS62uBZZ6xb4PPAB82Mv/qXOuC9gkIuvJf7h5dGQjH5082bqGu/Y/SGWsindPejU1scoRaXdNxzp+f+hPlEXLePO411AXqx2RdovFqradfHHbA0Qlwk3NlzK7Ytyg62zLtvOLA48gwOvHn09V9Ggp6doj+/n4hodwzvGFeedySs34QbfbH9a1tXDDc8tI57J8aeFpnNswYdjaumLyeGIR4WAqzel11Vxw/5O0pzP81xmzefnkwfdzf9h2JMVVf9rMoY40XzlnKq+dWTci7Y5FRKQSiHh/vyuBlwOf7lXsbuB9IvJT4BzgsO3fNAzDMIwTk6K61IpIM7AYeByY1P2BxPu3+zyRsL1ABvDwkedZuPhtlNVN5YWOzSPW7vLO51mw8LXUNMxlXcfGEWu3WPypdRPjLriSqsUX8uChLUNT56Fn2Fdbzd6aKu49tDywzB8PbqHqvKXUXHQZvzmwbUja7Q+/37OL2FnnUnPlNdy+d3iNDUSEyxrH8/rpk3lgzwE441Rq3vxavrftwLC2q/njthayC+dQ/9438j+bWkas3eHCueN/9YNJwF9FZAXwBPBb59zvReRGEbnRK3MPsBFYD/wv8PfDcJuGYRiGYYwBimYaJCJVwC+BDzjnWo7h6tXvvUAicgN5C34ak+WUxdNHlcmFuIR2Kfmbls6mfY6vfc/PwxwxfTGotJbRhklMdau6SGdOOLlsFg+t+CkRhOmTFoe6tObbCr53/57G4Ap63/nJyZncv/YuBGHehGspizqS2s1VH3SvZb39uEfdP1pGq910tRw1kwsur4n55LJKFqqlmqp8tJd77QVVU3jmkd8jwBlNl/SMC59brnqWYXXpfqmPV9B5eCvgmFQ+jRpvvFbHUz1lLqpt5IFHHwDgpXNOJ+bJMbW8NhYiJdWS17iSvGqX2kxGjW8lD76oYTy3P/EoHcAVc+f3ONJGI8FizbSWjyots44hTDaq5bgXT6rlfx5fTueyFbxq0dQeyakuE9VuvSpfIsH1Oz3uQxylL5pSwc1/fIGO59bx3tPriJXln4FPUhtSf7arcO/ZTLAmXcuAI/rZee2E1X08DLdpkHNuI3BaQP4tKu2A9w5jGIZhGIZhjBGKMuEUkTj5yeaPnXN3eNm7u23zRWQysMfL7/deIM/Y4laAhdV1J4RBxSW1izmtcjZxSVIWSY5Yu2dXn8L88maS0TjlkbIRa7dYnFM7g3kVExCi1MSO/35bMx383/7HEOA19edRE8nLaJc2NAeWX1LbyPdPuhwHVCXS/OuGZ8g6x8dnzWNc4vied0s6wydWb6Yzm+NTc2cxsezoek6va+DXSy4k7XJMKg8+wiSMtnSWj6/cxuF0jv84dRJNlf2P86S6Kh649HQ6szmmVo/cn6d5dWU89arZtKayzKgf48cTO8gNzjTIMAzDMAxjyBjxT1aSX8r8DrDaOfcV9au7geu99PXAr1T+dSKSFJGZ5A8Sf2Kk4h0L1MVqRnSy2U1trPqEmGx2Ux+vGNRkE+CeQ8vZPqGWF8dVc9/hFZxdM5+za+b3GC8FURcvoz5exo92rmfFhAZWTWnkf188fhnzbRu380jdOJ6e0cxXNm4NbzeRYEJy4Pf7o417eKCyluXz5/Dp5wdu/V6XiNNYXoTxnIjSVDWwyfVoxQ3iP8MwDMMwjKGkGCucFwBvBZ4TkeVe3seAm4HbReSdwFbgdQDOuVUicjvwPHmH2/f25VAL4S61WvbYmS58uOxUh9tnVJlciERTQqSYWvOaC5HX+tLq0khInfouctph1SdRPLaTLeSlod0kJFhWqhdGtMpZx6PvPRaihM6FfG4Nk1XqOLVcNhWSTvvcboPbUmajxNQNJELki9GQ2Doy4W+TUIdYRcTnyJokdWAnOEdT2WQmV7QDEFcyy6QatwmVnpRIkNu9C4lGmVhR2SNv9clNI8FyVl1mUlkMt2s3kkwyKVmQg4rkVHkC8zPaqbmzMBnVst4JyTi5rfuItHcwKRnreQ/pca8lphrf2NL3paSt0XiwI67LBktnw2TcmnhZQcbsa6usqxBbrBBzurWiJ+1zu1VEVPz6fuPlnUe1JSFS5ePFpo2GYRiGYYwWiuFS+1eC92UCvDTkms8Bnxu2oEqQPan9/L7lceqjNVxeez5RGdlzAI1grh53EvWHyhCBl4TIaMN4w5RmavfEyboc10xuPO4YrpvRSFUsSmc2x9WNx++/dSST4TPrNtCayfKp+TOYUp6fgL56+ngSkQgt6QyvmzV87rYjQSrr+H8PtbGpNccXLi5nwfiibXs3DMMwDMMYk9inpxLl14cfRhrnsWHfC6w8so7TqhYUOyQDiEiEi+rneuk+F+p7XStcNakJgFik/2fMBtVzTVPeBDqdPn5V/U9e3MbDFdVEGhr44oZN/OfJ84C86+zV0/LHmYSdjTlW+PnqDn57pJyqc5r5h/tXcu/raoodUp84HDlb4zQMwzAMY5RQshNOwfVI8pxyl+3KxAPT2oE265PjHX8MWsqnJaNapqtlnFpOGOZ4qh1WtZ2rLpNGqIlUsOvwi3R1Hqauvpx4xPkcUnU9EiKvDXdtLTSs40z5HECVfFLlhsl9O7NK6qykkWEOtFpGqx+RltHqe0xGtAw4uB80nUomGQ1x34VwqbEmoa6viBXkmtppVsudtbw2od1lVX5cObVqB1qdr2Wc2j01oyTC+vnpevTY7ewq7KdsTxXSla6G3L4XoaOd8bEkaa/eSGehTNznKKsca7X0V6V9bq4hktQwuawLcWHW6HpiWjqr08lCesq4DJ1PdhFZvYPG8gguk2/bqf4MkwprB914RUFGG4kfPQkPGzvHizPTIMMwDMMwRgklO+EEWH7oEF/f+iKnVVXzvuaZHOPolZLjVQ0X83Tbaurr5jCnfHqxwxkVtGTa+H3Lw5RH4ryy/nzKIqVhEDOStGfSfHnLao7kMnygaSH/JFGOZNO8cea4Iam/I5Pjo8t3sbcry+fPGMf06uI+o8vmJPj2ZSm2HDzAm+YXVjfTWcdHnzjIhrYsnz5lHAvqR5d5lk03DcMwDMMYLYxx//9j85G1L7DpvPO4/eABnjw4cofIjwaSkQTn1ZzGgoqZxQ5l1PDn1idoaWhgW5njkZZnix3OmOQXOzfxVHkZLzRO45btL/CyCU1c2ziTeGRo/pT8eP1+/hirZPmi+Xxs+b4hqXOwvGJ+gr8/t4yqROEe79zQyh2tCZ47/XT+6anREWc3Dsh5strjeRmGYRiGYQwlJb3C2ZBIsH3jBrIdHVRKBV2pOF3qgPq0knFqSW0YPtlbiGQvF5LW+CSEIeX743jqu1pd7Je/9i2Rjat4Yipdpvbf6fJaOlsQCkJcpaPadVZLYdW17f2Q0aZ9UuRCvpbU+mS0+r58LrWO6mgZ21p2kcl0UFs+kVjE+fpctxUPcRjOHUNSq9PlSjqr0/p5RH39ruSyUZ0OlssmdDpZcFjVZNRY1+7GmliIk22HcqBNZwt/JqoilWQObiHX2UFDLNEjD0+rtrQ0V6Ndc8Oks5OqIffiQSK5HJPLIz3lctkQ11l1X7lcf9xilaxXx6Ocb0WlM23KjTZdqH98MkL2wEFk/YtMKo/2/G2IKVfbeIVyuFV16phdiMOtYRiGYRhGqVDSE85vnLSI3+zYyey5pzKncvSbfRST3alD3H7gKSbEKrluwhJinqvtrq4WfrjvacbHKrh+4hnEI2P3A/JLa8+moW01lYkYi6vmFjucQXE4neamtZvJOvjsyVMYXxbv+6Ih4PLxM0lGorRn07yycdqQ1/+qmTVEgP2dXbxx3vghr3+ouGBKBefWHWbzth184LzjdwweLuw8TcMwDMMwRgslLaltSCR4W/MMzqwbvR9cRws/3v8E+2fMZHmklccOb+jJv23vU+yYPZOnYh08dGjDMWoY/cQkylnVJ3NG9bwxv5/31k3beXj8OB6f2sh/vbBzxNoVES4dN4NXTJwzZDLa3vVfO6uWv13UQHls9P55+tXGVp6KVHFg6Tl8bMXok+u7QfxnGIZhGIYxlJTsCmfORejoyssCO5XcL6XkgQ7taqmuDTkm1OeIqdKZEDdajYS4v4bJbsUnDS2kfZJd7XYbVfVr59uQ+rWMNh7JURdNsHffdtJHDjNhQpzKWIayWIZx8QQ79mwn03qIhnEziEmOFMEOurEQ91rtQDtQGa2WzmpJbVw1nPA5vOp4gt1o9bNLa6dfda3rh3Mv+J+Tls5WxgsyV5+MVpSMVstZdVrJa5OJQj1aRjupXGD3Xlw0xoSqwqqzdqPNqr72jSdVj0ZLUl2Iq3J1WUfgtTHlQBvRDrQq35fWrrAh7rg5JTd16l50nJlM8Iq7litrfI7MWkarnlG2I9ioSLvjTqiEzMEWYhu20VjpSFYfydepZLRaptvtbgvg1ECLRL3nO6QutbYX0zAMwzCM0UPJTjhLje1dB/j5gWdojFXzN+OXEJWhXf3520nn8uDBF2gcP5kzagpSyb+ffCb37l9PXUMjS2qahrRN4/h5x+xGamN7yTjH62ZMLHY4Y55U1vGRv7Syuc1x8zlVzKk/tkT50ukVfCvr2NJykLedWxtYJpNzfOz+TtYedPz7BWUsmjAyf24d/i9MDMMwDMMwiolNOMcI39v7GF1zT+bFzauY2bqJs2pmD2n9ZZE4l487iWTUf0ZgWTTGKycuIJMdvfLGE5GoCK9rtonmUHH78+3c0VpJ7JSZvP+vz3HPK+v7vOaqmZWAf6VUc9fqFD/bXUbl+fO48U8reOiNVUMas2EYhmEYxligZCecOSe0ewfWp7UET8nidH4uREKo5bVapNcfGW3YFG2gslsRR020jG37tpHpbKWmqpwIkNAumyFthdWvJbUJlU4qV9RsTsuG+27B37dKOqvSqdzAZLS6jJbL+txoQ/K1FFb3ud6nFgvRHIdJkXu7B1cqGW2ZkobqLaJaRqvlplpGm1T1aLdYnxutktdq19ZMRsnEfTJrJZtW1+pxkEkXrtXyVF2mIlmQiWq3W+06G+aaq9MxnVaSWp9UPasltcpFWt2jdsTVZbTzrZbU+t5X0WCpbUMiRmZ/K2zcyYRy6ZHYaqktce0YrJxvk4V70eUnVELqYDvRF3YxoQIklvNiKJSPJPL1SEhcx4tJag3DMAzDGC2U7ISz1Pi7SRfwl0NrmdhwDgsrpxQ7HMMoKa6YVcHXs45trYe4/vzqIalz6aw4377MseHALq6/uHxI6uwvZv5jGIZhGMZowSacY4SKaILLx53iP4PQMIwh49q5eYnsUK42Xr0gb0CkDYdGAptwGoZhGIYxWijZCadzQspzucwqOah2T9WSUV1GO9ZmfY6mkcB8TZgDbbYfHzh1Cb2TUrekZaL9OXkxEiIx1XJcLfnUcWo5sb5ff5kCncpVVDvQ6nR6EG60Oh3Vae3KqzorTEYbUWV6S5eD0PVXKfdZ8DvThqHrTSi5rJbRarlsUslNYyGOsqnOgpNqTj0PLfVMKCmsKImpz/1V9VGY06xzWiqs5MjqXuJaRqvuJVam3Hpj/v3BPfWr96GemDkVp5bRZkOcaX0SXDVAtEOxLoOSE+s4tbusL12uHGiVjNY3mdT1qz6MxAv3LqofpNtdOjJ0E0QnjpwMrUTXMAzDMAzjeDEnGMMYBg6nu/j3LU/x+S3LaMuk+r5gjHAkk+Vjz2/g/c+tY3fH0NxXRybHPz+2j7c+sIdtbX1P4MPoyub45KqtvOvpDWxt6+r7AsMwDMMwDGPYsQmnYQwDP9y1mufG1/FMfSU/3rmu2OEMGT/avJ0/VVby+Iwm/uOF7UNS5w/XHObuXDWPz57LR5/ef9z13PniXn6TjfL03Hl87LmdQxLbWCXnncV5PC/D0IjIFSKyVkTWi8hHAn6/QEQeFZEuEflgMWI0DMMwRjelK6mlIJPNhrinahltmFy2PzJaTbYf7rVh6LZ80tkQ91pNxCcr1dcqp9IQ504tq8z0Q1qs00eUe2ibOtw+o2SSqVyINDfks61POutze3WB+RrdbpjUVl8bJq/VkuNqJaNNRINloQBRJYusjSXIHtyHy2apr6jvcU1NKBdgLT0tU/LXeCJ4lS9MSupzi+2HjFbXEws50iPM/bWxOgfbD0IqxaTKHJXVbfkySjKqHWhFxaYlslpGW5eIk9t/GERoiMXIdOXlwqmugmxYx+xztVXjtTYWJ3f4IJGdu2mIR3ukrhlVj08SHCKjjVV1FPIrOwvXJpVMNaMGjn5cPhmtKp9QY1H91XXdIQzx1myHSWqNwSMiUeC/gcuAbcCTInK3c+55VewA8H7gVSMfoWEYhjEWKNkJp2EUkzc3zqd+70aiEuHayU1DXn9LOsPnXthMxjk+MX8G9Yn+7OgdPNfNGk88sp/WdJa3LZgwJHX+zawacI59ne28eWbjcdfz8snjSOccuztTvPXUoe/zwZDNOT7xG8eavcLnXgmLpgyf+ZfD2YTTGCrOBtY75zYCiMhPgWuBngmnc24PsEdEXlGcEA3DMIzRjk04DWMYiEUiXDNpDgBROf59iWH876ZtPFhbB/E4X93wIjctnDXkbQQREeF1M8cDEIsOzX2JCK+dUwdApuv4Vf4iwtVN+UlwvB9mTiPJnc84frqhnKmXTONdP1nHw/8yvO3lQgywDGOATAVeVD9vA845nopE5AbgBoDp06cPPjLDMAxjzFCyE07npEdKm/ZJapW0UEnzUtng/P6sE7gQuWmuH/Xoa7XcL1g86a8n7KO5ltGGSUD9zr06TiWvDZEiaxltR4iMtjOnZclaXluIQbvRJrS5p0/mGuwumw2R+8a1dFZJXGMhkmMtuy1TfVWpHFhj/TwmQ8ua41o6q91cletseVlBrqmlrdr9NYzxFQ534CASizKxylFe2X5UGZ/jq+6jZN9mPzoG7S4bS2oH2oJ8N6ri1+3mlBOsltFm1RjSktd0KtiN1vceywa/OyKRQruprmRPWkuL9TqwT0Zbc6RwL3VKRlsIrSB/7U1MjS0lu9XXjmsQOg6lOLThELMqQ+oZQmyF0xgigpbij+vbDOfcrcCtAEuWLLFvRAzDME4gSnbCaRilzN/NaaR60x4yzvHmOUMjbR3rpHI5bl67lZ1daT42bxrTKsr6fW066/j4fWm2HHbcfK1jzsShlby+ZFGEW96QZd2u3fzdNebVZowZtgHT1M9NwI4ixWIYhmGMUWzCaRhjkKgIb5o1CYBIJNzI6ETizhd38+u0IDPn8snVa/numfP7fe1PV6S5a185E86YxN/fsYE/3jj08b3qTG9lNj28izu2h9MYQp4E5orITGA7cB3wpuKGZBiGYYw1SnrC2S219MlHlTSvU0nztHxUIyF7oQYqow0r73OUDWypV3lfcMFOttqN1rlguWzW5xzbd/9oGW1nVry6HRlVZ9r1LaPVaLfYeMjB9z4ZrVePc46ME8Sznk1GgyWyuq90WzGV9sto06rMwGW0MTXx0zLahKo3qZxptVRVyz41sWQK59kFl9e29eRn0yGS1HTwW1pLecPwSY1jmZ52ExWFmOOVBQfXaHkh36l2s13BUt4wGW2YG20up9PamTY4XU45udYDRHbvoS4WI5s9+j0dUc87VlGQzo4bn6HzYCctmw8zsxqk3BvjXWpcamdaNV4jZapMPGRlVGnAnVZMd9c5xHPQnE04jSHAOZcRkfcBfyC/0+M259wqEbnR+/0tItIIPAXUADkR+QCwyDnXUqy4DcMwjNFFSU84+8OWjn386uBzzEiM46r603omMcaxWdO+mZ/u+xNVsUrePuEaamNVI9Lu1q4XufvQ70hEynhN3bXUxepGpN1isfJAB6+//0VyDn5xeSOLJ/RfJjoY1h3u5G8e2EpHOsv/vayR8ydXAHAknePjj7TSknZ84dIkk6rCdhuPPC+bOJFULsferi7evGDmgK49daqj/XAXBx7bzbteDQM5ojiTdXzy144X9gn//mrHvMbiS2bdMJ6nKSLTgB8AjeS/V7vVOffVXmWWAr8CNnlZdzjnPj1sQRnDhnPuHuCeXnm3qPQu8lJbwzAMwwik+J+Misz/7P4rB5rnc3/7Rta229aU/vJQ20pmn/IaEg0zeL59/Yi1+3THSmY3vYy6+rms6Vw7Yu0Wix9sOEzy6rOoeM15fHttW98XDBE/33SI7IWnU37dZXzzhUK7ty5v43fRWh5vauJjf+k4Rg0jj4jwismTeXtzM5WxgX2XdtNvcpQ31XLSG+Zz858HNon+5bIcP91Qzq5Fs7jhRwO6dJhwOLLH/eoHGeBfnHMLgXOB94rIooByf3HOne69bLJpGIZhGCcoJbvC6SjIW7V8VEtnM7kIFdEkRw7tJpvuIhlJknNCNETe6EKksxp9pZan6hgiIYuovlZDJLhRnxNqVuUXrtaOsrmQE+WdT/4a7EDbquSNnVktk4Q5iWk8/PxvyOUyTB53LZ3ZSI/UFvwyWh2BvvdEREthA8P0udFmnDAjPp1Htt+PcznOr7uKmLh+OdNqyW656rfyEEdY3W9OLxb1ilO3p8dNQrnUahltRJXPZUNcZMsLUs8rZkb45d1PgAhXXVLT4wyrrxX17HUMoiS7LkSS2ltG283SaUm+88DTSER4xXk1xMry99BQnSWzow2XyVKvFltzyq1Yx5ZRrrMDldEGSWLzZZR7cqZQv97Lqu8rpvohrmS0kfKCy+7iqVHu3NTJoU2HmVIFuRavj9R9+dxoVbquKkLHoRQtW1qYVU7hjRwmo02pZ5EdHkntcOKc2wns9NKtIrKa/PEZzx/zQsMwDMMwTkhKdsLZX94z6VL+emgNTeMvYEaZuX32l/OrFzO3bAZRSVIdHRk5LcApFSfTlJhKWSRKdbR6xNotFlfNquSRcQkcMLN+5OSrF0+t5OFrp5HKOuY3FmZD71hcRjTSSUtXir9bPALnewwx6azjpsdb2HokxxeuzNHckJ9Qvu/iKPFomvV7d/OJdw/sz+Llp0T4WkeWDXv38O5XF19i7IDcCM1gRaQZWAw8HvDr80RkBXlX0w8651aNSFCGYRiGYYwqTvgJZ22sgleMPyN0xdIIZ0K8wWcUNFLUx+p9q5elzoza7lW8kTWCaarqbrewEhgR4e2nlwP+ldKxws/WHOH2lgTlJ0/l73+3mnvenF9pFRFuvPD4/xy+7uziTzQL5Kecg2C8iDylfr7VO0PRh4hUAb8EPhBgEPM0MMM51yYiVwF3AXMHE5RhGIZhGGOTEp5wSo/7qnZhzYTIU/vjRhsqow1xzezPB3JdJhMmddRp3a5Kd2b7fpRabplVssSWdEGWeCgdLKPV0ta0z+FW56u2tKGndqOVYBmtz402ZC6p3Wi1jDYRVVJWJVlNqnSldo0NkUxrGa0eDvr5RnqNEy1ljimpbkS1oa/PZIIlo2VKRhtNFBxls0qSqmWx2qVWO6ZqWaxT496FSKsjqu8icSXRVjFE4oU6s53J4Hi0dFaVGYyM1if71g63meCxriW1MdUPUeXQW1eZI9OSIrXjEHWJCDkvpuyRgj5YdH9WFp6LxEKcftUxJ04PLSWjdVoerNxuXff7cIgn74N0qd3nnFtyrAIiEic/2fyxc+6O3r/XE1Dn3D0i8k0RGe+c2zeYwAzDMAzDGHuU8IRzdLM7dZDfHV5GY6yOl9edSWSE3HF3p1r4+b4VjItV8TfjFhOVE943yjiBeOXcMtrTHWxr3ceNLy9MMjM5xyf/0sHmFvj3ixMjKl8eDobzHE7JW3l/B1jtnPtKSJlGYLdzzonI2eQN6vYPW1CGYRiGYYxabMJZJP5v/0MwbSFbtq9kavs4Tqkc2DEOx8ttu5/g4LRZvLBzA00tGzi/1lRuxomDiHDdSfkjXqJlBTOn21d18sPdlcQWNfOee1fy+9dXFCvEscAFwFuB50RkuZf3MWA69ByZ8VrgPSKSATqA65xzJ44O3jAMwzCMHkp2wulcwX1Vu7z61vO0PFJLZ3URVUZf2589nxIiH83mhDJJ0NK2n0y6k4QkyPT6KBbRMauG9aEFHWHSQi0V1vm5CElJkm49QLqrnUy8gv1dCQ6mCvW0K6dZLZHVaR2rrj8esliq7z2qyvRHRqvr1NLiMOlsmZKIagfaeD8k0xlfnwcfDyG9zFi0dFbLa31yUCVZzupYk8oxVclxtctrurMgQw1zl9XpnFNS1RB5qk9Gq6WnKq1lpWGy3pyS9aa1XFaVT6UK+TqeMBmtdqDtUtdmssErjpGQfojFtaQ2WBKcU3FWZCNkWzuQXQepTUjPvUXU/arHi+tSfav/YOQC5LK9yQ2vmsHhcG74Vjidc3/lKL/mo8p8A/jGsAVhGIZhGMaYoWQnnKOdN49fyqMtq5hQew6zy6eOWLtvm3A+Dx56nsqKxSyqaO7JP5Rp4YGWx6mIVHN+1TlETGrbL1rSKb65fTVxifBPzbMHfP6jMfykc47PPb2fbR05PnNuBdNr4keVuWpWBV9IO7a27uU9Lx177ru9GU5JrWEYhmEYxkCwT8dFoipazmX1S0b8Y2FlNMlV4xbTmvY/+t8dfohc9UR2tG2joWMtiyoWjnBkY5Pv7HiBxxqqIZ1m3PaNvGfGvGKHZPTi5+sO8+PWOJF50/jHh9dw55XjjiojIrxhQf54n1hZ51G/H1s4m3AahmEYhjFqKOkJZ7eMcKCy2LC1vVyIBLe3c2k3uvaMktfpj4Jhcl/dlnaF7XR9rzzq+9VSynYlXWxL63xBXILOrsOkMx2kIwkOpSCVU7JK7UCrQkgoXawykQ2V1+ZccFqjpba6mniIjLZaSSa1G62/Xe2sGyyf1ketaMfaY7kNV0ajuNYWXCZDebKGjCcV1c7IenxUlrf3pKM+Ga2SmKaOXoGDXg60qmOcvjYTLD31vQeUXFjLazUZJeXVaHfcnJJ0awdaLYvN+lx5C2mflFnlp0JktNplV8uaRUma46p/tEttZSSOa2vD7T1MdTTac2/afTdeoY5+iQePIdcV/Fy0zBgtow1xQ/Zd6/XPkB4x4yA3jJJawzAMwzCMgVDSE06j/1xStZSVHStIJqYxPTGr2OEMmJZMO3cefIqaaIzXjz+DRGRkhvbbp8yjdtdGYokIr50yfUTaNAqkczm+sWUDe9MpPjJ/MpPLy44q84oZNRzJ5NjRfpB3n3v06uZgyeYcn30gw9qD8JmLY8weN7Ydbg3DMAzDMIYSm3AaACQjSc6sPJvOYK+cUc8dB55i48Rqsm2HaDiwmqvGnzIi7SYiUd4wJe/0G5Mx2nljmN/s2s6vc2lkRhOfWruRW0+ff1QZEeENc+oBiCVCztIcBHeszPDtTeVUnDmTv/vt89z/ttEw4bQVTsMwSpfmaY1s2ba72GEYhtFPSnrC2SMjDJGrhbnRhuF3jg3+QKflml0hLpVh8jmf1FbLEp3O71t6l3V+uWxPWikFtdNsu/qhI6ulvIWIEsoqt0y03DQ4hjC5rI4t6pPOardRVJlCvnaaLY8VJndV8TTVsSjZI4fJdh6hMjmeqLgel+Le7fpiULH5hJTq2rh61pmcfzLRqRxo09nC2ykeLdRWo2W0Ee1kGyw91cTjhQmSdnbNpZVEO2Q86ba0HDcSL/Sdz023K1hGGybZ1e67mXSw3DTMgVbLZX316HsMka3HlBS5OunIHWgncvgwNXHpkdLq9080G/w+1O61omW0Sq7tlITYhUjqKyNC5kgX6T0tVMcLDrcS8jfC517b/X4eQkntcLvUGoZhFJst23bjvnTsMvLBkYnFMIy+KekJZ7HY2XWQXx98lsZ4PS+vOw2RIdyfNQK051pY0/U4CaqYmzhrTDjWXjdhMQ37V1OZHMfSejPuOVG4snESndkce7s6eeeiYElzOuf4/Mq9bO/IcNOSGpqqgifHx8vlc+J8uTPHhgPbufGqoyW9xSBnK5yGYRiGYYwSbMI5DNy296/kmk9i3ZZnaWpv4KTKsbW379nOh0iSYF9uC5WZWpriC4odUp8kI3FeOeHUUMMnozQREV7TNAWA8niwu+wvNx3kx21CZO5M/umJdfz8JZOGPIbrThkdE80CfSs2DMMwDMMwRoITbsIZJj/sD1paGQ2R4LZnoyQjMVqOHCKb6SIpR3exhEjzUkoum1IywLRPUltIayfVtMrvUFsJdX5W6V+7VJlOJZ1tz2ZwLkonHWRIIcTIOIdeEwqT0eps3W7EJ5EtpPWT0GViOq3kjZWxQqVlSlaZDpEu+2S0IWV8qPI++aRyY+3dVlzJaCvjqUI6WZj8RLREUzvkqucdjWpJsXYHVpLUdLDLqy6v3Vl9DrQqBl2Plulqt1Vfu0r+mlPjUjvKhsWjZbTpTGEUdSknXu3oq+vRkmAtUU4kCv2cVHsy9bVatlqVEFx7J+5AK1UJiCbz12hpsYh+Y6l6wr7C6Ie0Xbv4alyQ07TNDw3DMAzDKFFOuAnnULK96xB3HFhJc6KOqxtO6pHO3jDpYh44uJqJdWczp2JKkaMcOPMSF7Et8xwTZDYTo2PPsdYwNFfPqKE1nWNH+wHed2HtsLaVc45/f7iTFw45PnVekln1xfgT63CYgZVhGIZhGKMDm3AOgm/sepj0/FN5/oXlzEjWc2rVVABqYxVcO+FMOrOjwa1y4MQlycz4EuJjYO+mYfSFiPCmuXmX2mSyvY/Sg+Ou1Sn+Z3OCsjNm864/ruK+N1QNa3thuDAZgmEYhmEYxghT0hPObomdT8YY8PtjoaWzOh2L5CiPxOhoO0wukyIhsaNsOvR0rT8y2k4tV/RJXoOls/oIk07lLpsN+bCZVtaxaafThUpj98+6tQAAHsRJREFUapIZU2ZHuqfCPJB0bNGQMqH5qrMSSgpaodJxn8Orkn8GV+mXWKr8XIhjbTZknOjnmOjlPFqX6FJxFyKJhEiu9TiIqvLRWPBdaOdYLX+NKolpNBEso9Uy0TCn2YySfep8X/9qSa12/s31LYXtUrLbtGorzI1WXxuLFPpEy2i1S612wY0pybWvb+O6f4L72SdzVRLzgQrwy6NRsh0pMvtbqYxFehxr+2SoXWrNNMgwDMMwjFFCSU84h5sPTLmAPx5Yw9RxZzCvcmiNSIzRS3u2izsPPEVMolzfeDJlkaF1PdW0pbN84YVt5Bx8aO50quND+5btyGT5/OoddOQcH5rbRH1i+O7lRODlsxN8oSPH+oPbeM/LKosWhx2LYhiGYRjGaMEmnIOgIV7BdZPOINUfQxqjZPjVwWWsqYmSy6T4zb7nee3E04atrW+u38Fvk9UQiZJcv42PL2we0vq/s343d0kF1FbCC9v595OHtv4TDRHhTaeUA+Ayxfq74MAmnIZhGIZhjBJKdsLpYECiMl1WS2fDpJGpEGmhJhIio834JLIhclmV367OpD+U0o6ywfLAiNK85pR0NhtihRlVwkEto41HCvcVVfnaUTajqtS9oMuE7SbTZeIqXaYcVePaXTVEdij9kCOGXZtW+R1qgqDlzZqJZWnixMl2tZLLpCmPV/ikvkFElDQ0Hi+4qibKCnJc7aqqpa1lkSiuvQOJRKiISY9MVktwYzHttqodYrV0VktqC2/7BHFcRwcCJImRTse9a5VLrc9ZV8WppLbaHXegMtqIT7auJNSqrzQ6hoiW4CrpbExdq2XGup+dks6iJ4fqr6JowXYkeCT7JpbavVa774a42nbn90febxiGYRiGMRYp2QlnKXMkd5g1XU+SpIaZ8TN63HGNoaUj18mfDz9BXKJcWnM2cU86+4r6M6g49BwxiXL1+OF18b1h9lSSm3bgnOMds6YOef1vbZ6CbN5BRy7DW+cU57zYdC7H/25bx8FMivdNn8W4xGg703Ls4eycFcMwDMMwRgk24RyDPNPxZypyFeziBSoidTTGZhc7pJLk/sNPsCORJZs5Qlnrci6uPQuARCTG5Q2LAYjJ8LqeJiIR3jW7Ceh1VuQQEYsIb/cmstmQVd3h5te7t/Ab10mkcRIdW17g5rmnFiWOksHZHk7DMAzDMEYPJ8SEM0xOqfHJaEPK+FxMQxxNw9r1SWdD8v2OtYV6WpTW9lAmhSNKii6yZMggtLsMEYI1rDn1Q0yVSYiSQ4bKaAv1aPlrVrvRRnS/hUheVVrXk1DXJpXsMRoiY/b1c4gksz9rvfo5prLBMtojGQHiZDJHyGa7SCSiRCPuqNhyIS1G1OSwLFlwWC0r7yxcGyJV1fikoVo+qiSsGi3HzYY402azSuaq87WMNkQ6q/O1lFe73WoZbVbJSiXE8bkiKriuLlz7ESpjkEwcLaX1SU7VtVp2q9Pik9GqflbPWCI67fMxLiRVvmiXZJ+jb7B0NiztY1iktOZSaxiGYRjG6OGEmHCWGvMTl7At/RxN0kx9ZFqxwylZzq86i7Ijy6lMRDmn+pTjrqctneFL618kivDxU8dRGR+b57MOF1dNaqIjl+VgR5p3zJ5b7HB8ZHOOzz/SyabD8KmLYkyrG/3PzoFNOA3DMAzDGDWMmQmniFwBfBWIAt92zt1c5JCKRkLKmZU4m4zt0xpW4hLnnKqzqEkM7sP71zZs47fV1ZDNUr12Dx89efIQRVgaRER43ZRmAJKxYKOgYvHL51P8z+YEFadO44bfr+N315UXOyTDMAzDMIwxxZiYcIpIFPhv4DJgG/CkiNztnHt+MPX2x422P3JcjZYlpkOcabM6rZrtUjLa1nThF22Zwi/01Ecbg3QRLLGMq0ccD5HRagfa/tyt9igSdYXO13LcmFKLahltWVTJffsho9VrSwOV0ep6ulT/d/meRSFdqyaZVTHlnNrLkTainoGWjCaUY2pF3EF7Jy6XJVkmPWMqlw1eLdMOtFrGGVd1aomplolqaavOD5PRZnxyWS27DXZh1u2KGrz9cWrWMuOouq9oNHhCr6WqkUjh3uM+N9pCWsJcZPV7WMuG1b1rCa4eT2UxyHZmyLR0UBaVHpdbn0Q2xJk2FKeltsO1b9ZWOA3DMAzDGB2MiQkncDaw3jm3EUBEfgpcCwxqwmkYI8H7502mfP0uIsC75k0qdjjGAHjl/AT72rvYfGg7779yrLjnOnCmfjAMwzAMY3QwViacU4EX1c/bgHOKFIthDIhkNML75k8BIBINPjvVGJ2ICH97xliZaBawPZyGYRijl2SMPo+0m9E0ic0v7hqhiAxjeBkrE86gd+VRX+GLyA3ADd6PXdev+u7KYY1qdDEe2FfsIEYQu9/S5kS73/lDW52tcBqGYYxWujLgvnTsMvLB3SMTjGGMAGNlwrkN0HasTcCO3oWcc7cCtwKIyFPOuSUjE17xsfstbex+SxsRearYMRiGYRiGYQwHY2XC+SQwV0RmAtuB64A3FTckwzCM0YgDZ5JawzAMwzBGB2Niwumcy4jI+4A/kDcrvc05t6rIYRmGYYxKnElqDcMwDMMYJQyXJ/+Q45y7xzk3zzk32zn3uX5ccuuwBzW6sPstbex+S5shvt/cIF7HRkSuEJG1IrJeRD4S8HsRka95v39WRM4YopsyioA9b8MwDGOwjJkJ50Dx9nOeMNj9ljZ2v6XNkN+vc8f/OgbqTOQrgUXAG0VkUa9iVwJzvdcNwLeG9N6MEcOetzHSNE9rRET6fBmGMbYYE5JawzAMY1TQnzORrwV+4JxzwGMiUicik51zO0c+XGOQ2PM2RpQt23b36d4KIB8c/lgMwxg6Sm6Fsy/5z1hERKaJyP0islpEVonIP3r5DSJyr4is8/6tV9d81OuDtSJyefGiP35EJCoiz4jIb7yfS/Z+vQ9pvxCRNd5zPq/E7/efvLG8UkR+IiJlpXa/InKbiOwRkZUqb8D3KCJnishz3u++Jn1+ve8G9V8fBJ2JPPU4yhhjA3vehlEkus/q7OvVPK2x2KEaRp+U1Aqnkv9cRv5/ek+KyN3OueePfeWoJwP8i3PuaRGpBpaJyL3A24H7nHM3e5PrjwAf9iRP1wEnAVOAP4nIPOdctkjxHy//CKwGaryfP0Lp3u9Xgd87514rIgmgAvgYJXi/IjIVeD+wyDnXISK3k7+fRZTW/X4P+AbwA5V3PGP4W+Slio8B9wBXAL8La7S9vf0Py5Y9MX4QcZeJ/5iWW5Xktz9nIvfr3GRjTDBkz1t6nZOtv4gZ5YyVM4HHSpzQR6z9Xb3sT7khqKsn1qGMayDl+mLLtt2ISMk8/1GGxTpwZgRlltSEk/7Jf8YcnjRpp5duFZHV5L9BvhZY6hX7PvAA8GEv/6fOuS5gk4isJ983j45s5MePiDQBrwA+B/yzl12S9ysiNcDF5L9AwDmXAlIiUpL36xEDykUkTX5yvQP4KCV0v865h0SkuVf2gJ6piGwGapxzjwKIyA+AV3GMCadz7oohu4mj6c+ZyP06N9kYEwzZ8x6r52SPlVjHSpxgsQ4HYyVOsFiHi9Eea6lJakte2uN9gF0MPA5M6t4n4/070StWCv3wX8D/w2+bWar3OwvYC3zXkxB/W0QqKdH7dc5tB74EbCX/Rcph59wfKdH77cVA73Gql+6dXyx6zkT2VuKvA+7uVeZu4G2S51zyz9f2841N7HkbhmEYg6bUJpwlLeUSkSrgl8AHnHMtxyoakDdm+kFErgb2OOeW9feSgLwxc7/kV/vOAL7lnFsMHCEvtQxjTN+vt2/xWmAmeflopYi85ViXBOSNmfvtJ2H3OKru3TmXAbrPRF4N3O6cWyUiN4rIjV6xe4CNwHrgf4G/L0qwxqCx520YhmEMBaUmqS1ZKZeIxMlPNn/snLvDy94tnhugiEwG9nj5Y70fLgCuEZGrgDKgRkR+ROne7zZgm3Puce/nX5CfcJbq/b4M2OSc2wsgIncA51O696sZ6D1u89K984uGc+4e8pMMnXeLSjvgvSMdlzE8DNPzHkvHHo2VWMdKnGCxDgdjJU6wWIeLUR1rqa1w9kf+M+bwXCm/A6x2zn1F/epu4HovfT3wK5V/nYgkRWQm+fPRnhipeAeLc+6jzrkm51wz+Wf4Z+fcWyjd+90FvCgi872sl5Lfd1yS90teSnuuiFR4Y/ul5FdPSvV+NQO6R0+a2Coi53p99TZ1jWGMScbSObtjJdaxEidYrMPBWIkTLNbhYrTHWlIrnM65jIh0y3+iwG3OuVVFDmsouAB4K/CciCz38j4G3AzcLiLvJP8h/nUAnuTpdvKTlgzw3jHg6NkfSvl+/wH4sfdFyUbgHeS/ECq5+3XOPS4ivwCeJh//M+S/mauihO5XRH5C3iBovIhsAz7F8Y3h95B3vC0nbxYUahhkGIZhGIYx2pC8GsYwDMMwDGPoEJEryB/5FAW+7Zy7udfvxfv9VUA78Hbn3NMjHOM08kcXNZI3qbvVOffVXmWWklcWbPKy7nDOfXoEw9SxbAZagSyQ6e1KORr61ItjPvAzlTUL+KRz7r9UmaUUqV9F5Dag2y/iZC+vgXzMzcBm4PXOuYMB1x5zXI9AnF8EXgmkgA3AO5xzhwKu3cwxxsoIxXoT8C7yxogAH/Nk+r2vHbE+PUasPwO6lWZ1wCHn3OkB125mhPo17O/TaByrfWETTsMwDMMwhhTJn4v9AupcbOCNTp2L7e3T/wfyk6NzgK86584Z4TgnA5OdOucaeFWvOJcCH3TOXT2SsQXhfdhd4pwLPG9vNPRpQExRYDtwjnNui8pfSpH6VUQuBtqAH6gJxxeAA+qc5Hrn3Id7XdfnuB6BOF9OfqtRRkQ+D9A7Tq/cZo4xVkYo1puANufcl45x3Yj2aVisvX7/ZfKO20d9ATKS/Rr294n8MXqjaqz2Rant4TQMwzAMo/j0nIvt8mcLd5+LrbmW/Ac+55x7DKjzPmCNGM65nd0rgM65VvL7ycfqsUswCvo0gJcCG/Rks9g45x4CDvTKvpb8+ch4/74q4NL+jOthjdM590fPQRrgMfzGckUjpE/7w4j2KRw7Vk8l8HrgJ8MZQ384xt+nUTdW+8ImnIZhGIZhDDX9OT93VJ2xK/5zrntznoisEJHfichJIxuZDwf8UUSWicgNAb8fVX3qcR3hH95HS79C+DnJmtHWv39L+L7+vsbKSPE+EXlWRG6T/LFovRltfXoRsNs5ty7k90Xp115/n8bcWLUJp2EYhmEYQ01/zpAdNefMyrHPuX4amOGcOw34OnDXCIenucA5dwZwJfBeTxqoGTV9CuAZ4V0D/Dzg16OpX/vLqOlfEfk4eZO5H4cU6WusjATfAmYDpwM7gS8HlBk1ferxRo69ujni/drH36fQywLyitavNuE0DMMwDGOo6c/5uaPijF0JPue6B+dci3OuzUvfA8RFZPwIh9kdyw7v3z3AneRlc5pR0aeKK4GnnXO7e/9iNPWrx+5u+XGvc5I1o6J/ReR68qY3b3YhZiz9GCvDjnNut3Mu65zLAf8bEsOo6FMAEYkBr8FveOVjpPs15O/TmBmr3diE0zihEZFpIrLJc/xCROq9n2eIyGQR+c0A6/uSiLxkeKI1DMMYM/TnXOy7gbdJnnPJm3TsHMkgvf1aQedc6zKNXjlE5Gzyn532j1yUPXFUesYhiEgl8HJgZa9iRe/TXoSuFo2WflWEnZOsKfp5757z6IeBa5xz7SFl+jNWhp1e+4dfHRJD0ftU8TJgjXNuW9AvR7pfj/H3aUyMVY1NOI0TGufci+QlH91W0TeTt53eAvwz+W/kBsLXgY8MXYSGYRhjD8/UpPtc7NXA7d55szeKyI1esXvInzu8nvzf2r8vQqjd51y/RESWe6+resX5WmCliKwAvgZcF7aqNMxMAv7qxfEE8Fvn3O9HYZ8CICIV5B0y71B5o6JfJX9O8qPAfBHZJvmzkW8GLhORdV7cN3tlp4jIPRA+rkc4zm8A1cC93ni9pXechIyV4YrzGLF+QUSeE5FngUuBf+od60j36TFihYD9xkXu18C/T4zCsdoXdiyKccLjyRWWAbeRPy9qsXMuJSIbgYXOuS4ReTt5F7AocDL5fQgJ8n8IuoCrnHMHvPqWAa9wzu0a6XsxDMMwDMMwjNGErXAaJzzOuTTwIeA/yW/ITonITOCgc65LFT0ZeBN5vf7ngHbn3GLy35K9TZV7mvy3UoZhGIZhGIZxQmMTTsPIcyV5B7XuA4AnA3t7lbnfOdfqnNsLHAZ+7eU/BzSrcnuAKcMXqmEYhmEYhmGMDWzCaZzwiMjp5DXw5wL/5G1y7wDKehXVq5059XMOiKnflXnXG4ZhGIZhGMYJjU04jRMazwHsW+SltFuBLwJfAl7Av2o5EOZRBDc4wzAMwzAMwxht2ITTONF5F7DVOXev9/M3gQXAEmCDiMwZSGWeAdEc4KkhjdIwDMMwDMMwxiA24TROaJxztzrn3qB+zjrnznTOPUjeevztXv73nHPvU+WanXP7An53NfALz47aMAzDGCOISNY7dmCliPzcO9ZjONpZIiJf89JLReT8AV5/k4hsV8ckLBeRupCydSLy9+rnKSLyi0HdQKGuV4nIoqGoawBtvl1EvhGQf5OIfLBX3mYRGT/M8Xysn+XuCXtGqszbRWTA/g8i8gsRmSUiSRH5vTd+9TO/VUQWq5/fJyLvGGAboeM07JkMBb3b9Y7VeduxrhlA3T1nrYtIs4h0qPfTLarcmd7RLutF5GueMg6vv3/m5T8uIs0h7Wz2rl8uIk+p/AYRuVdE1nn/1qvffdSrd62IXK7y/6TLjSVswmkYITjn7gQ2D/CyGPkjUwzDMIyxRYdz7nTn3MlACrixrwsARCTWd6kCzrmnnHPv935cCgxowunxn16s3a9DIeXqUGdxOud2OOdeexztBfEqYEQnnKMFyRMB+jXhdM5ddYxn1M3bGaDhoIicBESdcxuBy8kf8XYqcIP3+9OAiHPuGXXZbcD7e9fVB0s5vnE6WHztOuducc79YIjq7n3W+gb1ftLv/W+R78+53usKL/+d5E8zmEP+lIPPH6OtS716l6i8jwD3OefmAvd5P+N9iXMdcJLX1jdFJOpd80OKeLbuYLAJp2EcA+fctwdY/uf9+J+KYRiGMbr5CzDHW4W4S0SeFZHHRORU6FlRu1VE/gj8QERmiMh9Xrn7RGS6V+513orTChF5yMtbKiK/8VZEbiRvVrdcRC4SkU3e1gxEpMZbHYn3J2AROUlEnvDqelZE5pI/EH62l/dFbyVnpVf+7d69/dpr930i8s8i8ox3rw1euXeJyJPePfxSRCq8VadrgC96dc/2Xr8XkWUi8hcRWRAQ49ki8ojXxiMiMl/Fcod3/ToR+YK65h0i8oKIPMhxHDnm3fNqEflfEVklIn8UkXLvd3O8VaMVIvK0iMz28j/k3fOzIvJvver5Jvnjz74DlHv3/2OvzF3e/a8SkRtUDJtFZHxYLCLyWvJbeX7s1fcKEblTXX+ZiNwRcHtvBn7lpdNAOX4Tw88An9QXOOfagc0icnZAX71S8qt1z3j9MilonPaz3//ZG/srReQDKv9tXr+uEJEfDqRdUSvZInK6N06fFZE7xVv5E5EHROTz3nvhhWPE+zfA7/u4h8lAjXPuUeecA35A/osWgGuB73vpXwAvFcmvfvYTff33e9X7U+dcl3NuE7Ce/HF8AHcDbxxAG6MH55y97GUve9nLXvay1wn9Atq8f2PkP8S/B/g68Ckv/yXAci99E/nVpHLv518D13vpvwXu8tLPAVO9dJ3371LgN6qeD6oYvgu8ykvfAHw5IM6bgO3Acu91v5f/deDNXjpBfvLRDKxU1/b8TH5FbT1QDUwgf9zXjd7vus+lBhinrv8s8A9e+nvAa9Xv7gPmeulzgD8HxF4DxLz0y4Bfqlg2ArXknd63ANPIH1G21YsvATwMfCOkTz7YK28zMN675wxwupd/O/AWL/048GovXQZUAC8HbgWE/MLMb4CLvXpywLm9x4z6ucH7t5y8eeC4AcTyALDESwuwBpjg/fx/wCsD7vtB4BQ1bv8PeIb8meHX4I3dgOs+DvxLQH49IF767/DGX1D/qmve3vuZAGeSH/uVQBWwClhMftVuLTC+V3/1q139M/AscImX/jTwX6ofu6+/CvhTQMwzgWW93hdHvL57ELjIy1+irwcuovDeXQk0qd9t6L6vXm1tIv8FxTLgBpV/qFe5g96/3+geE97P38H/PluHek+OldeAZCCGYRiGYRglSrmILPfSfyH/Qe9x8ishOOf+LCLjRKTWK3O3c677CKzzgNd46R8C3St0DwPfE5HbgaAVqt58G/h/wF3AO8gb2wXxn865L/XKexT4uIg0AXc459b1Y8HlfudcK9AqIr3Plz7VS58sIp8lL8+tAv7QuxIRqSIvffy5ajMZ0F4t8H3Jr746QK/e3uecO+zV9zwwg/wk7QGXP/8aEfkZeSf43riQ++vO3+ScW+6llwHNIlJN/suAOwGcc51eGy8nP+nslqFWkZdSbgW2OOceC2kL4P0i8movPc27bn+vMkfFclTQzjlv9e8tIvJd8uMraO9iz5nhLu8d8SbvHuLkn9M1IvIVYDrwA+fc3d51e8gbJPamCfiZt7KXID9ZOh4uBO50zh3x4rmD/GTNkfe56PbAOHA87XrvwTqX99uA/Arhz1WR7vdaYP9y9FnrO4Hpzrn9InImcJfk5cpBb6DuMXWs32kucM7tEJGJwL0issY591DQffWz3u6z3nuPq1GNSWoNwzAMwzAKezhPd879g3MuxbE//B05Rl0OwOX3gv0r+cnHchEZd6wAnHMPk58MXUJ+b16/j9hyzv0f+VWtDuAPIvKSflzWn/Olvwe8zzl3CvBvHH1GNeQ/Tx5y/n2lCwPKfYb8JPdk4JW96tKxZFX7YZNJzX7yq2SaauDQMeoOm40L8B/qPuY4577j/S70mYvIUvKrtuc5504jP2EN6quw++zNd4G3kJdQ/twFmxEGnRkO+X1+3yc/UU0BbyA/DrsJOy/86+RXK08B3h1Sd384Vt8GPc+hareb7j4O619fv7m8fHW/l15GfrVyHrCN/GS4myZgh5feRv593b2PuxY4QC+cczu8f/cAd1KQx+72Jtjd0t09vesNaBPG6FnvNuE0DMMwDMMI5iHy++S6JxT7nHMtAeUeIW/0gVf+r941s51zjzvnPgnsw/9BEqCV/MRI8wPgJ+QnHP1GRGYBG51zXyO/1+vUkPoHSjWw01s1e7PK76nb65NNIvI6LxaRvGFNb2rJy4HBc4Hvg8eBpd7Kchx4XUi5h8iv5lV77b8GWOGcy4ZV7MW8TURe5V2TlLwz8R+Av/VWbRGRqd7qVBBpKeyxrSUvi2yX/P7Vc/txfxrfs/ImKjvITxS/F3LNavJHsfXg7WW8mvw4qiD/5YHDP4kLOy9cP5/rw2LrBw8Br5L8ft9K4NXkVQP3Aa/v/uJFvH3CA23XWwk/qPZnvpW8FLa/+M5aF5EJ4hnzeO+jueTfSzvJr/6f6+3PfBuFPbN3q1hfS15C7rw61nj/VqoxWUl+5XxlwPXX96r3Om88zvRiecKrQ4BGBm5oWXRswmkYhmEYhhHMTcASEXmWvAHP9SHl3g+8wyv3VuAfvfwvSv5IhJXkP4Sv6HXdr4FX9zJj+TH51bqfHCOubiOV7lcz+VWslZ4seAF5CeV+4GHPuOWL/b9tH58gP/G7l/y+wm5+CnzIM3qZTX4y+k4RWUF+z961AXV9AfgPEXkYiAb83of3gf8m8nLhP5HfCxdU7lnye9/+6t3/jeT3AvbFW8nLYJ8l/6VBo3Puj+T3Qj4qIs+RN4QJm2zdCjwredOg3wMxr67PAMeS3gbxPeAW73mWe3k/Bl50zj0fcs1vye8J1nwS+Kw3+fkD+X2Iz+F3ZL2AfH/25ibysui/kP+CpJugcap5u4hs636RX637HvmJ0uPAt51zzzjnVgGfAx70xslXBtHu9eTfX88Cp5Pfx9kvPKmvPmv9YvLPcQX5532jkvu+h7zUfT35lc/fefnfAcaJyHryjrfdLrPjKazwTiI/Jld4ffFb51y3UdHNwGUisg64zPsZr49uB54nP6beq744ORN4LGS1e1TTvUHXMAzDMAzDKDKSdyy91jn31mLHYhQXyZ9v+YyS9Pb+fTlwP/l9gqGrub2uWQz884k+vry9tmc65/61z8IDq/dqYJanNBhSROSr5PeO3zfUdQ83ZhpkGIZhGIYxChCRrwNXknfXNE5gRGQZ+T2j/xJWxjnXISKfAqaSNzXqD+PJr1qf0Djn7uxrT/Vx1vuboa5TsXIsTjbBVjgNwzAMwzAMwzCMYcL2cBqGYRiGYRiGYRjDgk04DcMwDMMwDMMwjGHBJpyGYRiGYRiGYRjGsGATTsMwDMMwDMMwDGNYsAmnYRiGYRiGYRiGMSzYhNMwDMMwDMMwDMMYFv4/AN7icKf4q0UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = 500                                    # location to estimate\n",
    "y = 500\n",
    "\n",
    "ix = geostats.getindex(nx,xmn,xsiz,x); iy = ny-geostats.getindex(ny,ymn,ysiz,y)-1\n",
    "\n",
    "plt.subplot(121)\n",
    "GSLIB.pixelplt_st(kmap,xmin,xmax,ymin,ymax,xsiz,vmin,vmax,'Kriging Estimate','X(m)','Y(m)',feature + ' (' + feature_units + ')',cmap)\n",
    "plt.scatter(df['X'],df['Y'],marker='o',s=5,c=df[feature].values,edgecolor='black')\n",
    "plt.scatter(x,y,marker='o',s=50,color='white',edgecolor='black',linewidths=3)\n",
    "\n",
    "kest = kmap[iy,ix]; kerr = vmap[iy,ix]\n",
    "pvalues = np.linspace(0.01,0.99,100)\n",
    "print('Kriging Estimate = ' + str(round(kest,2)) + ', Kriging Estimation Variance = ' + str(round(kerr,2)))\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.hist(norm.rvs(loc=kest,scale=math.sqrt(kerr),size = 1000),bins=np.linspace(vmin,vmax,40),color='darkorange',edgecolor='black',density=True)\n",
    "plt.xlabel(feature + ' Estimate and Uncertainty (' + feature_units + ') at Location (' + str(x) + ',' + str(y) + ')'); plt.ylabel('Probability')\n",
    "plt.title('Kriging Estimate and Kriging Variance-based Uncertainty')\n",
    "plt.xlim([vmin,vmax])\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.1, wspace=0.3, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration / exercise of variogram calculation and modeling for spatial continuity analysis and spatial estimation with kriging. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
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 },
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}
